a methodology to evaluate the pedestrian accessibility to ......the arcgis software, and it...
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A methodology to evaluate the pedestrian accessibility to transit stops Application and analysis of results from the study case of Nomentano district in Rome
Faculty of Civil and Industrial Engineering
Master degree in Transport Systems Engineering
Module: Transport Policies and Terminal Design
Candidate
Nicola Favaretto
1720786
Supervisor External Supervisor
Prof. Maria Vittoria Corazza Prof. María Eugenia López-Lambas
Prof. Belén Martín
Ing. Javier Delso
A/A 2016/2017
SUMMARY
1. Introduction .................................................................................................................................. 4
1.1 Rationale ..................................................................................................................................... 6
2. Key Concepts ................................................................................................................................ 8
2.1 Road Network in urban environment ............................................................................... 8
2.1.1 Transportation and Urban Planning ............................................................................. 8
2.1.2 Historical Review ............................................................................................................ 8
2.2 Walkability ........................................................................................................................ 11
2.2.1 Defining Walkability .................................................................................................... 11
2.2.2 Values, Constraints and Criteria of The Walkable City ............................................. 11
2.2.3 Example of Walkability Index...................................................................................... 14
2.2.4 How Land Use Affects Transport Choice ................................................................... 14
2.3 Accessibility....................................................................................................................... 15
2.3.1 Defining accessibility .................................................................................................... 15
2.3.2 Perspectives ................................................................................................................... 17
2.3.2.1 Review of Accessibility Measures ....................................................................... 17
2.3.2.2 Conventional Form of Accessibility Measures ................................................... 18
2.3.2.3 Importance of Perspectives in Evaluating Accessibility .................................... 20
2.3.3 Factors ........................................................................................................................... 22
2.3.3.1 Transportation Demand and Activity ................................................................. 22
2.3.3.2 Mobility ................................................................................................................. 22
2.3.3.3 Transportation Modes .......................................................................................... 23
2.3.3.4 Information Provided to User ............................................................................. 23
2.3.3.5 Integration among Modes .................................................................................... 24
2.3.3.6 Land Use Factors .................................................................................................. 24
2.3.3.7 Connectivity .......................................................................................................... 24
2.4 Equity................................................................................................................................. 27
2.4.1 Definition....................................................................................................................... 27
2.4.2 Typologies ..................................................................................................................... 28
2.4.3 Evaluation ..................................................................................................................... 29
2.5 Transit Oriented Development ........................................................................................ 31
2.5.1 Concept Delineation ..................................................................................................... 31
2.5.2 Service Area .................................................................................................................. 32
3. Thesis Purpose and Methodology ............................................................................................ 36
3.1 Aim of the Work ............................................................................................................... 36
3.2 Case Study: Nomentano District in Rome ....................................................................... 37
3.2.1 Brief Presentation of the Neighborhood...................................................................... 37
3.2.2 Description of the Analyzed Area ................................................................................... 39
3.3 Methodology ..................................................................................................................... 42
3.3.1 Street Network Analysis .............................................................................................. 43
3.3.1.1 Street Classification .............................................................................................. 44
3.3.1.2 Intersection Density.............................................................................................. 45
3.3.1.3 Pedestrian Catchment Area and Network Connectivity ................................... 46
3.3.2 Transit Accessibility Index ........................................................................................... 47
3.3.2.1 Introduction to the Indicators .............................................................................. 48
3.3.3 Ideal Point Method ....................................................................................................... 50
3.3.4 Pairwise Comparison Method ..................................................................................... 52
3.3.5 Questionnaire ................................................................................................................ 54
4. Application: Case of Study ........................................................................................................ 55
4.1 Street Network Analysis ................................................................................................... 55
4.1.1 Road Classification ....................................................................................................... 55
4.1.2 Intersection Intensity Analysis..................................................................................... 56
4.1.3 Pedestrian Catchment Area as Indicator of Urban Connectivity .............................. 60
4.2 Accessibility Index ............................................................................................................ 61
4.2.1 Number of Lines ........................................................................................................... 61
4.2.2 Frequency ...................................................................................................................... 63
4.2.3 Land Use Entropy ......................................................................................................... 64
4.2.4 Level of Service ............................................................................................................. 67
4.2.5 Pedestrian Catchment Area as Indicator of Bus Stop Accessibility ........................... 69
4.2.6 Inhabitants Served ........................................................................................................ 70
4.2.7 Level of Comfort ........................................................................................................... 73
5. Analysis of the Results ............................................................................................................... 74
5.1 Evidences from the Questionnaire ................................................................................... 74
5.2 Criterion Weighing ........................................................................................................... 76
5.3 Multicriteria Analysis ....................................................................................................... 78
5.4 Accessibility Evaluation........................................................................................................... 82
5.4.1 Best Results ........................................................................................................................ 84
5.4.2 Worst Results ..................................................................................................................... 86
5.5 Alternative Indicators .............................................................................................................. 87
5.5.1 Cost Distance Function ..................................................................................................... 88
5.5.2 Potential Accessibility Indicator ....................................................................................... 89
6. Conclusion...................................................................................................................................... 92
6.1 Further Improvements and Future Research ......................................................................... 93
Appendix A: Bus and Tram Lines .................................................................................................... 95
Appendix B: List of the Stops ........................................................................................................... 97
Appendix C: Land Use Entropy Calculation ................................................................................. 103
Appendix D: List of the Indicators for each Stop .......................................................................... 107
Appendix E: Ideal Point Method Calculation ................................................................................ 113
Appendix F: Python Code ............................................................................................................... 119
List of Figures .................................................................................................................................. 121
List of Tables .................................................................................................................................... 123
References ........................................................................................................................................ 124
Acknowledgements ......................................................................................................................... 132
4 1. Introduction
1. INTRODUCTION
After World War II, most of the urban planning theories and practice were affected by traffic
based-development: transportation analysis rarely took into account the quality of the
environment and the user perceptions, as it focused on motorized vehicle; pedestrians were not
considered a priority and even negatively considered because they did slow down the flow of
vehicles at street crossings (Ramsey, 1990). The consequences for the urban environment and for
pedestrians have been enormous.
However in the last two decades the attention to pedestrian environment and walking activity
has been increasing thanks to policies oriented to identify and develop the concept of
walkability as the foundation for the sustainable city. Enhancing non-motorized modes is often
one of the most effective ways of improving motorized transport (Litman, 2003). The
transportation planning field starts treating walking as a mode of transportation. Shifting travel
from the automobile to walking is a core strategy for reducing greenhouse gases, regulated air
pollutants, road infrastructure expenditures, traffic fatalities, and other social, economic and
environmental costs of automobile. These impacts imply worldwide interests, thus many
nations are developing design policies towards a smart growth. The entire European policy is
described by the White Paper, a document published every ten years where all the objectives
and solutions are established. The 2011 White Paper is looking toward a competitive and
resource efficient transport system through the execution of several objectives in different
transportation fields. Most of those targets are oriented to a reduction of emissions and an
efficient multimodal travel. In this view, the concept of walking and cycling assumes a key role.
In particular, cities strongly suffer from congestion, poor air quality and noise pollution. So,
according to the objective of cleaning urban transport and commuting, public transport choice
and as well as the options of walking and cycling must become widely available. Encouraging
non-motorized transport and increasing public services accessibility is a fundamental part of
the urban mobility. Non-motorized transport as bicycling and walking contributes to develop a
“green” transport that reduces congestion and can register a substantial decrease in air and
noise pollution (Newman and Kenworthy 1999). Moreover walking may promote sociability as
it represents an equitable mode of transport available across classes, including children and
seniors. Studies indicate that residents living in walkable, mixed-use neighborhoods are more
likely to know their neighbors, to participate politically, to trust others, and to be involved
socially (Leyden, 2003). Walking is the most used and, at the same time, underrated mode of
transport as often trips start and end with a walking trip. Walking is the way that allows people
to reach everyday activities and basic needs, from public places to every kind of points of
5 1. Introduction
interest. In particular, every public transport user is a pedestrian for the first part of its journey
while reaching the public transport stop. Thus, the concept of Transit-oriented development
(TOD), which represents an approach integrating transport and land use planning with the final
objective of encouraging the use of public transport (Schlossberg, 2004), can be linked to
walking. A key component is represented by the pedestrian access between the transit stop and
the surrounding area. The basic idea is that pedestrian hostile streets will necessary have a
negative impact on the pedestrian public transport choice. Therefore, it is fundamental to focus
on all those urban design features that influence in some way the walking choice and behavior
of pedestrian, to point out what promotes and discourages the walking activity in order to set
the design criteria of the urban environment.
However the relation between the built environment and the walking behavior is complex, due
to the fact that it is strongly affected by the individual perceptions, the attitudes, lifestyle and
transportation alternatives. The concept that expresses this relation is the walkability of an area
(Kalakou, Moura, 2014).
The aim of the work is then to try to gather the concepts of walkability and TOD evaluating the
accessibility of the bus stops of a neighborhood in the city of Rome.
As mentioned before, the mode choice process for a user is strongly complex and it
comprehends both quantitative and qualitative (perceptions) considerations. Then a correct and
complete approach suggests that local walkability must be analyzed quantitatively and
qualitatively in order to provide planning and evaluation tools.
The final map can describe in a unique way the pedestrian accessibility to the public transport,
taking into account quantitative and qualitative analyses. The map will suggest and
comprehend physical and perceptual considerations, describing the urban layout and the
preferences of the public transport users. It will be straightforward to point out which stops are
more accessible and preferred by users as well as the ones most affected by fragmentation and,
according to this, assess some interventions in the design of the environment to improve the
accessibility, as moving the bus stop itself, refining the facilities and so on.
The second chapter presents the fundamental concepts treated in the work from a deep
literature review, it explains the importance of the road network in the urban environment, the
walkability, accessibility and equity concepts and their relation through the notion of transit
oriented development. The third chapter shows the purpose of the work and the methodology
used, the first part deals with the street network analysis, the central part presents the
accessibility index while the last part reveals the methods chosen for the calculations. The
fourth chapter concern the application of the methodology and the presentation of all the
6 1. Introduction
assumptions made case by case. The fifth chapter shows the analysis of the results of the work
through the implementation of the Ideal Point Method and the Pairwise Comparison Method.
The final chapter draws the conclusions based on the results and the utility of the work for
eventual future work and research.
1.1 RATIONALE
The work started with a deep analysis of the literature about the concept of accessibility and
walkability, as to assimiliate in a correct way the matters that characterize them and to discover
the several ideas they comprehend. The initial intention was to focus on pedestrians, since it is
an actual argument and it involves interesting concepts as accessibility and equity, often
neglected in the evaluation of transports. However from the research, it was clear the strict
relation between pedestrian environment and the urban structure, so the collection of the
literature moved towards a more integrated way in order to inglobe these two fundamental
arguments. The urban layout, mainly defined by the road network, strongly influences the
pedestrian routes and their relation with motorized vehicles. Specifically, in the modern view of
a sustainable city, the main goal is to move people from private car to public transport or non
motorized vehicles.
Here is the innovative and distinguish point of the work. The analysis takes into account the
fields of walkability, connectivity and transit service, and merge them to make an overall
complete evaluation of the accessibility of the district. The concept of accessibility, as explained
in chapter 2, is usually considered as an abstract argument and it is not easy to describe it with
concrete indicators. So the aim of the work, that is also its innovative distinction, is to describe
the accessibility of bus and tram stops starting from objective indicators touching the concepts
of road network, transit efficiency and walkability.
The methodology, deeply treated in chapter 3 (see picture 13), is carried out through the use of
the Arcgis software, and it describes the stops within the Nomentano district in Rome. The total
number of stops is 231 and the 7 indicators defining the index have been chosen thanks to the
analysis of the literature available and in order to achieve the final aim of the work. The overall
evaluation is then developed through a multicriteria analysis and the indicators are weighted
using a pairwise comparison model, fed by a questionnaire provided to 41 experts in
transportation engineering, professor, master and PhD students. The final result is then a single
value describing the accessibility of each stop, according to their characteristic and to the
surrounding environent.
7 1. Introduction
Moreover some additional indicators are calculated, in order to give some practical and
alternative examples of the potential usage of the data found. The potential accessibility
indicator is calculated, which relates the road network and the stop position with the
population within its service area. Finally, a cost distance function is applied to the network, to
calculate the cost of the routes from each portal of the buildings to the closest bus stop.
Below is the rationale graph resuming the main steps of the work.
FIGURE 1: RATIONALE GRAPH
8 2. Key Concepts
2. KEY CONCEPTS
This chapter presents and analyzes some fundamental concepts used in the project, as to clearly
understand how they can be inserted in the argument treated and which role they have in the
literature and in the study itself.
First of all a brief history background of the shape and structure of the city, i.e. its form, is
presented, in order to clarify the relation between the urban plan, the citizens’ needs and the
progress of the society. Then the attention is shifted especially to pedestrians, as to introduce
the key arguments of the work: walkability, accessibility, connectivity and Transit Oriented
Development (TOD).
2.1 ROAD NETWORK IN URBAN ENVIRONMENT
2.1.1 TRANSPORTATION AND URBAN PLANNING
The road network represents the basic skeleton of the urban form (Schlossberg, 2006), so the
shape of the city and the urban edge development follows the social, industrial and economic
growth. As a matter of fact, the evolution of street patterns has implications for the quality and
character of new urban environments (Southworth and Owens, 1993).
If at the beginning of the last century the transportation planning did not assume an important
role in urban planning, due to a movement of people and goods mainly based on walking,
horse drawn cart or carriage, in the 1930s the attention began to be split between two different
branches, one focusing on the technical aspects of transportation planning and engineering and
the other focusing on micro variables characterized by the use and form of local places and the
built environment.
Southworth and Owen studied (1993) the form of the evolving metropolitan fringe identifying
and underlying principles and spatial typologies and analyzed patterns of growth, land use and
street layout for all the last century. It is interesting to observe how the urban planning changed
as the scale of development has grown. As said before, street patterns represent the first marks
of settlement and they both divide and link several urban spaces. They characterize and
strongly affect the citizens’ habits and jobs, since they determine where residents can go, how
fast and what they can experience during the trip. That is the reason why the observation of the
street pattern growth over time allows understanding how a community has grown.
2.1.2 HISTORICAL REVIEW
Walking was the fundamental mode of transport in cities before the automobile era, the urban
pattern necessarily reflected that. The streets of the preindustrial cities were walkable, allowed
9 2. Key Concepts
to give free and easy access on foot or by slow moving cart. Activities and every kind of public
service had to be connected by a continuous path network.
The gridiron pattern is the most common method of urban planning before the World War 1
when pedestrian was the main mode and presence of cars almost neglected. The street design
was not yet automobile oriented, it created instead the most walkable neighborhood. The
system was very simple and it reminds of the ancient Rome urban structure, developed on two
perpendicular main road, cardo and decumanus, creating a pattern equal-sized square or
rectangular blocks. This kind of structure offers the shortest trip lengths and the largest number
of route choices as it has a high number of intersections and point of access. With the arrival of
the automobile, high speed transport and the quest for efficiency killed the walkable city. This
fact reflects somehow the development of society and way of life during the last century,
keeping on asking for a life characterized by the efficiency and the speed. Thus, the walkable
city has been set aside at the end of the 1920s with the rise of this research for the innovation
and the perspective on the future, represented in other fields by the avant-garde as the
Modernism. Modernist planning and design separated pedestrians from the automobile,
putting them aside to raised plazas, barren “greenways,” and sterile pedestrian malls
(Robertson, 1994). In the late postindustrial city it become really difficult and unsafe for the
pedestrian to travel freely, the fundamental skeleton of the street of most of the residential areas
began to turn more complex and based on automobile needs. Moreover the society moved
towards a research of internal and more private subdivisions: the blocks of gridiron pattern
were stretched into long rectangles to reduce street building and to create quieter
neighborhoods. Dated from the 1950s, this pattern was called fragmented parallel, it is still
characterized by rectangular corners, but the blocks are narrow rectangles L shaped. This limits
the degree of interconnections and the number of connections with respect to the previous
pattern. As said above, the reduced number of access points tends to point out the attention
towards a self-contained private subdivision. The transition to an automobile oriented pattern is
more evident when the blocks got “warped”, with consequence reductions in intersections,
street lengths and access points. The most recent structure is characterized by the social requests
of privacy and safety, the community streets are almost all curving loops or cul-de-sacs with a
network internally focused as to provide quiet streets and relatively safe for children. The lots
started to be larger, desirable for a higher income market. Moreover with the construction of the
modern freeways, the main objective was often to link them with the local communities, rather
than collect the different parts of the municipality itself. So the strongest connections became to
arterial streets or to regional highways, the streets were no more directional and they began to
10 2. Key Concepts
end to loop back on themselves. The congestion problem raised consequently, auto trips
increased and concentrated on the few main arterials. The squared blocks that identified the
gridiron pattern disappeared and started to assume odd shapes and to be penetrated by street
stubs. The attention to pedestrian is minimum, this structure provides limited route choices,
few access points and a reduced pedestrian access.
This brief review shows how the pattern influences the quality of the urban environment. With
particular attention to pedestrians, residential neighborhoods are affected by a constant
decreasing of accessibility as a result of the spread of disconnected and closed street patterns.
Many studies confirmed this strong relation between the neighborhood structure and the
number of pedestrians. Handy (1995) found that people living in pedestrian friendly
community tend to make two to four more walk or bicycle trips per week to stores compared to
people living in areas served mainly by automobile oriented establishments. Bernick and
Cervero (1997) confirmed that people living in “traditional neighborhoods” are more likely to
walk to the market. “Traditional” neighborhoods means to be characterized by higher
residential density, a mixture of land uses (residential and commercial), and gridlike street
patterns with short block lengths (Saelens, 2003). Again, Krizek (2003) found that households
change travel behavior when exposed to differing urban forms. In particular, locating to area
with higher neighborhood accessibility decreases vehicle miles traveled.
However, there are a lot of other factors that influence the mode choice of the users, some of
them are more intuitive and directly linked to the pedestrian activity, others are more subjective
but they strongly foster the walkability of a zone.
FIGURE 2: EVOLUTION OF RESIDENTIAL STREET GRIDS IN THE LAST CENTURY
11 2. Key Concepts
2.2 WALKABILITY
2.2.1 DEFINING WALKABILITY
The concept of walkability can be easily expressed by the definition proposed by Litman (2003)
“the quality of walking conditions, including safety, comfort and convenience”. The definition
itself can seem simple and clear, however it includes several concepts that range in large fields.
This leads to a difficulty in finding in the literature a unique explanation of that concept. More
over the term is quite recent, so even if there is a vast literature on it, the concept is poorly
defined. A wide range of actors have been involved in pursuing the evaluation of the relations
between the urban environment and the pedestrian behavior, and all have a different definition
on how to measure walkability (Lo 2009). Since the aim of the work is to evaluate the
walkability of a district and to understand what mostly characterize it, it is necessary to define
the term in a correct way. To achieve this goal, different definitions and meaning of walkability
will be presented, as to clarify how it changes according to the target of the specific study and to
filter the indexes taking into account the most suitable for the work. The walkability of a
community has been conceptualized as “the extent to which characteristics of the built
environment and land use may or may not be conductive to residents in the area walking for
either leisure, exercise or recreation, to access services, or to travel to work” (Leslie, 2007) or in
simpler terms, “the extent to which the built environment is walking friendly” (Abley and
Turner, 2011).
In order to understand walkability, it is important to visualize the urban network introduced in
the previous paragraph. The first to do such thing was Lynch (1964), he identified five basic
components of urban form visualized in terms of walkable urban network: paths, edges,
districts, nodes and landmarks. Paths can be seen as minor roads, that is the ones used by
pedestrians, edges represent freeways or arterials that constitute an obstacle to pedestrian
movement, districts can represent concentrated zones of walkable urban form, nodes represents
street intersections and landmarks the key origins or destinations, for example the transit stops.
As briefly introduced above, this matter does not consider only objective variables, but it is also
strongly influenced by perceptions and subjective reactions.
2.2.2 VALUES, CONSTRAINTS AND CRITERIA OF THE WALKABLE CITY
According to Southworth (2005) “walkability is the extent to which the built environment
supports and encourages walking by providing for pedestrian comfort and safety, connecting
people with varied destinations within a reasonable amount of time and effort, and offering
visual interest in journeys throughout the network”. This definition is rather interesting because
12 2. Key Concepts
it introduces some concepts not directly related to quantitative analyses, but otherwise linked to
the user satisfaction and its relation with the environment, such as the pedestrian comfort,
safety and the visual interest. Then, streets should be safe and comfortable, easy to cross for
people of every age and degrees of mobility. Moreover urban environment should constitute an
esthetical attraction for pedestrian, with attractive natural sights or attractive buildings/homes.
Spaces are attractive with street trees and other landscape elements that provide continuity
between built form and the life of the place. The attention to all the benefits brought by the
increasing of the walking activity is fundamental to promote the walkability of a sustainable
city. Southworth’s study and many others researchers in United States focus on the idea that
walking can promote mental and physical health. Among the health benefits are improved
cardio-vascular fitness, reduced stress, stronger bones, weight control, and mental alertness and
creativity. In particular the attention on walking activity has become central in United States
due to the numerous health problems besides obesity, from mental health to cardiovascular
disease (Frank et al., 2003). Obviously, the cause of obesity must not be reduced to the built
environment: genetics, diet, and personal life style play an important role, as well. Anyways,
three quarters of United States adults do not get enough physical activity, and one quarter is
inactive in their free time (Ewing et al. ,2003). Many researchers found that only 30 minutes of
moderate activity as walking or bicycling is adequate for long term health, but only one quarter
of the U.S. population achieve this (Frank et al., 2003, Powell et al. 2003). According to what has
been explained in the previous paragraph, one of the widest and most publicized study
analyzed the relation between urban form and health (McCann and Ewing, 2003). The study
took into consideration health data of more than 200,000 people in relation to urban form in the
448 countries and 83 metropolitan areas they lived in. Residential areas were classified
according to a “metropolitan sprawl index”, which considered several characteristics of the
neighborhood: residential density, land use mix, degree of centralization of development and
street accessibility (length and size of blocks). The study concluded that there is a relation
between urban pattern, forms of physical activity and some health outcomes; in particular,
people who lived in “sprawl” areas were more likely to walk less, weigh more and have greater
incidence of hypertension than people living in more compact areas. Anyway Southworth
(2005) defined six criteria attributes in defining the walkability of a city: connectivity of path
network, continuity or the linkage with other modes, fine grained and varied land use patterns,
traffic and social crimes safety, quality of path and path context. The concept of accessibility
will be discussed in the next paragraph. Since every trip starts and ends on foot, it is important
to provide convenient and accessible links to other modes of transport with reasonable time and
13 2. Key Concepts
distance. A pedestrian network will offer full connectivity between all modes so that one can
navigate seamlessly from foot to trolley or subway or train or air without breaks (Garbrecht,
1981). A pedestrian district cannot contribute to a reduction in automobile use if it is not
supported properly by transit (Cervero, 2002; Cervero and Kockelman, 1996). Land use mix is a
key point for the attraction of an area, a walkable neighborhood should have an accessible
pattern of activities to serve daily needs such as shops, cafes, banks, laundries, grocery stores,
day care centers, schools, libraries and parks. The automobile oriented development of urban
and extra-urban patterns and land use policies have made walking inconvenient, unpleasant
and dangerous. This is the reason why the best understood and most fully developed aspect of
walkability is pedestrian safety. Thus, a lot of studies have examined pedestrian/automobile
accidents and their causes, safety standards and design handbooks have been developed and
are widely used (ITE 1998; Huang et al. 2000; Pucher and Dijkstra 2000, 2003; Huang and
Cynecki 2001; Ragland et al. 2003; Staunton et al. 2003; Zageer et al. 2004). One of the most
common trend to increase pedestrian safety is the usage of “traffic calming”. The purpose is to
slow down the automobile traffic through a variety of devices: chokers, chicanes, speed bumps,
raised crosswalks, narrowed streets, rough paving, traffic diverters, roundabouts, landscaping,
and other means. A study in The Netherlands found that traffic calming reduced accidents 20–
70%, depending upon the area (Pucher and Dijkstra 2003). The quality of the path plays an
important role in defining the walkability of an area. “Perhaps the least hospitable pedestrian
path is the auto oriented commercial strip, a treeless expanse dominated by several lanes of
noisy traffic, polluted air, glaring lights, and garish signs. The street has few, if any, designated
crosswalks and is much too wide for a pedestrian to cross safely. The chaotic frontage is poorly
defined, lined by blank big boxes, large parking lots, and drive-in businesses. Haphazard utility
poles and boxes, street lights, traffic control signs, hydrants, mail boxes, and parking meters
dominate the sidewalk, which is constantly interrupted by driveways to businesses”
(Southworth and Lynch 1974). This quote highlights the main features should characterize a
walk path, permitting a continuous trip to people of varied ages and physical abilities, without
gaps, pits, bumps or any kind of irregularity. The terrain also is significant according to climatic
features of the territory. Moreover landscape elements can help insulate the pedestrians from
the high speed traffic, trees can protect them from the sun and assist them in identifying the
street limit. Among the criteria identified by Southworth, the most problematic is the one
related to quality of the path context. The reason is due to the fact that it is really linked to the
personal and subjective perception, and so difficult to quantify. The context does not deal with
the single concept of connectivity, land use pattern, safety or quality of the path, but it
14 2. Key Concepts
incorporate all these manners together with the final goal of attracting the interest of the user.
Many aspects of the path context can contribute to a positive walking experience: visual interest
of the built environment, design of the street as a whole, transparency of fronting structures,
visible activity, street trees and other landscape elements, lighting, and views. Anyway, there is
not a unique correct approach, successful will vary by culture, place, and city size. The
important thing is to engage the pedestrian’s interest along the route.
2.2.3 EXAMPLE OF WALKABILITY INDEX
Another research by Frank (2009) developed an integrated index for operationalizing
walkability, based on transportation and urban planning literatures. The urban form variables
evaluated have been numerous, including land use mix, street connectivity, sidewalk
availability, building setbacks and many others. Anyway, the primary goal of the study was to
develop, test and apply an integrated method of sampling diverse built environments and
populations to optimize the power and relevance of studies of the built environment and
health. The four components of the walkability index include: net residential density, retail
floor area ratio, intersection density and land use mix. The first variable is the ratio of
residential units to the land area devoted to residential use. A low retail flow area ratio
indicates a retail development likely to have substantial parking, while a high ratio indicated
smaller setbacks and less surface parking, two factors thought to facilitate pedestrian access.
The intersection density (three or more legs) is an indicator of the connectivity of the street
network; a higher density corresponds with a higher number of possible paths and with a more
direct path between destinations. Land use mix represents the degree of diversity of land use
according to the following types of measures: residential, retail, entertainment (including
restaurants), office and institutional (including schools and community institutions).
2.2.4 HOW LAND USE AFFECTS TRANSPORT CHOICE
Other analysis on walkability and on the relevant characteristics to walk/cycle is proposed by
Saelens (2003). The research highlights some factors that influence the choice to use motorized
or non-motorized transport. Those are basically based on two fundamental aspects of the way
land is used: proximity (distance) and connectivity (directness of travel). Proximity is related to
the distance between the point where trip origins (where one starts the trip) and destinations
(where one is going). Proximity is characterized by two land use variables: density and land use
mix. Density, or compactness of land use, usually determines the frequency of walk trips. In
fact, the more dense is the area with many apartment buildings, the more convenient is to visit a
15 2. Key Concepts
neighbor walking. The second component of proximity is land use mix, or the distance between
different types of land uses, such as residential and commercial uses. In older cities there are
many residences above street-level shops, making it more convenient to walk to shops or to get
to work. In modern suburbs, different land uses are purposefully separated, so it may be
practically impossible to walk from one’s home to the nearest shopping center or place of
employment. High mixed use is characterized by a diversity of land uses within a small area. By
contrast, much modern development is based on single use, with land uses widely separated as
explained in the previous paragraph, resulting in a lack of land use mix.
As introduced at the beginning of this paragraph, the walkability concept is not unique defined,
thus this literature review supports to clarify the different fields in which it can be involved.
The walkability index used for this study will be introduced while explaining the method used
in the next chapters. In fact, in order to facilitate a better understanding of the indicators chosen
and to justify the procedure used, some other important concept are introduced now, even if
they have been already mentioned during the explanation if the walkability.
2.3 ACCESSIBILITY
2.3.1 DEFINING ACCESSIBILITY
Accessibility is defined and operationalized in several ways, and thus has taken on a variety of
meanings. These include definitions as the potential of opportunities for interaction (Hansen,
1959), the ease with which any land-use activity can be reached from a location using a
particular transport system (Dalvi and Martin, 1976), the freedom of individuals to decide
whether or not to participate in different activities (Burns, 1979) and the benefits provided by a
transportation/land-use system (Ben-Akiva and Lerman, 1979). Some researchers characterize
accessibility as a measure of the transportation system from the perspective of users of that
system (Ikhrata and Michell 1997). According to Litman, accessibility (also called access or
convenience) refers to the ability to reach desired goods, services, activities and destinations
(Litman, 2017). So, in general terms, it can refer to an elevator providing access to a rooftop or to
a library providing access to knowledge and information. In this view walking, cycling,
ridesharing and public transit provide access to jobs, services and other activities. Access is the
ultimate goal of most transportation, so it is intrinsically linked to the concept of mobility and
indeed it has been developed in parallel with it: while mobility concerned with the performance
of transport systems in their own right, accessibility adds the interplay of transport systems and
land use patterns as a further layer of analysis (Hansen, 1959). Since accessibility is the ultimate
goal of most transportation activity, transport planning should be based on accessibility.
16 2. Key Concepts
However, conventional planning tends to evaluate transport system performances based
primarily on motor vehicle travel conditions using indicators such as roadway level-of-service,
traffic speeds and vehicle operating costs; other accessibility factors are often overlooked or
undervalued. Such planning practices can result in decisions that increase mobility but reduce
overall accessibility and tend to undervalue other accessibility improvement options, such as
more accessible land use development. More comprehensive analysis could help decision-
makers identify more optimal solutions. Different planning issues require different methods to
account for different users, modes, scales and perspectives (Litman, 2008).
As already written above, accessibility is a multifaceted concept, not readily packaged into a
one indicator or index. However, Geurs and van Wee (2004) produced a checklist of
recommendations of how any accessibility measure should behave, regardless of its
perspective:
- Accessibility should relate to changes in travel opportunities, their quality and impediment: ‘If
the service level (travel time, cost, effort) of any transport mode in an area increases (decreases),
accessibility should increase (decrease) to any activity in that area, or from any point within that
area.’
- Accessibility should relate to changes in land use: ‘If the number of opportunities for an
activity increases (decreases) anywhere, accessibility to that activity should increase (decrease)
from any place.’
- Accessibility should relate to changes in constraints on demand for activities: ‘If the demand
for opportunities for an activity with certain capacity restrictions increases (decreases),
accessibility to that activity should decrease (increase).’
- Accessibility should relate to personal capabilities and constraints: ‘An increase of the number
of opportunities for an activity at any location should not alter the accessibility to that activity
for an individual (or groups of individuals) not able to participate in that activity given the time
budget.’
- Accessibility should relate to personal access to travel and land use opportunities:
‘Improvements in one transport mode or an increase of the number of opportunities for an
activity should not alter the accessibility to any individual (or groups of individuals) with
insufficient abilities or capacities (eg. drivers licence, education level) to use that mode or
participate in that activity.’
This brief list of definitions suggests how complex and widespread is the concept of
accessibility and how many different fields it touches. In order to clarify and make some order,
17 2. Key Concepts
in the next paragraphs the matter is described analyzing separately the different perspectives
and the affecting factors.
2.3.2 PERSPECTIVES
An accessibility measure should ideally take all the factors and elements within these factors
into account, thus applied accessibility measures focus on one or more components of
accessibility, depending on the perspective taken (Geurs and van Wee, 2003). In fact
accessibility can be viewed from different perspectives, such as from the perspective of a
particular location, a particular group, or a particular activity. It is important to specify the
perspective being considered when describing and evaluating accessibility. For example, in
building with stairs and no elevator may be easily accessible for physically-able people, but not
for people with physical disabilities. A particular location may be very accessible by automobile
but not by walking and transit, and so is difficult to reach for non-drivers. A building may have
adequate automobile access but poor access for large trucks, and so is suitable for some types of
commercial activity but not others (Litman, 2017). Geurs and van Wee identified four basic
perspectives: infrastructure based, location based, person based and utility based.
2.3.2.1 REVIEW OF ACCESSIBILITY MEASURES
Infrastructure based measures analyze the performance or service level of transport
infrastructure and it is typically used in transport planning. Several measures are used to
describe the functioning of the transport system, such as travel times, congestion and operating
speed on the road network. For example, the UK Transport policy plan (DETR, 2000) was
evaluated using congestion as accessibility measures. This type of perspective is obviously
really useful for operationalization and communicability, since the data are easily collectable
and there are already plenty of models available to evaluate this kind of measures. However,
these measures do not take into account the land use component and ignore potential land use
impacts of transport strategies, for example the impact of improved travelling speed of urban
sprawl.
Location based measures analyze accessibility at locations, typically on a macro level and they
are used in urban planning and geographical studies. Generally they can be distinguished in
two categories: distance measures and potential accessibility measures. Distance measures are
the simplest class, for example the relative accessibility developed by Ingram (1971), defined as
the degree to which two points on the same surface are connected. Distance measures are often
used in land use planning as standards for the maximum travel time or distance to a given
18 2. Key Concepts
location or to transport infrastructure. The advantages of this kind of measures are related to
operationalization, interpretability and communicability criteria, they are relatively
undemanding of data and are easy to interpret for researchers and policy makers, as no
assumptions are made on a person’s perception of transport, land use and their interaction. The
latter are also the weakness of these measures, they do not consider the combined effects
between factors and do not take individuals’ perceptions and preferences into account.
Potential accessibility measure estimates the accessibility of opportunities in zone i to all other
zones in which smaller and/or more distant opportunities provide diminishing influences. This
measure overcomes some of the theoretical shortcomings of the distance measure since it
evaluates the combined effect of land use and transport elements and incorporates assumptions
on a person’s perceptions of transport by using a distance decay function. Potential measures
have the practical advantage that they can be easily computed using existing land use and
transport data. Disadvantages of potential measures are related to more difficult interpretation
and communicability.
Person based measures analyze accessibility at the individual level, such as the activities in
which an individual can participate at a given time. It incorporates spatial and temporal
constraints and somehow describes the potential areas of opportunities that can be reached
given predefined time constraints. Person based measures satisfy almost all theoretical criteria
as a result of the disaggregate approach taken. Kwan (1998) demonstrates that space time based
measures capture activity based contextual effects which are not incorporated in traditional
location based accessibility measures as said before; this allows more sensitive assessment of
individual variations in accessibility, including gender and ethnic differences. About
weaknesses, the approach is demand oriented and do not include potential capacity constraints
of supplied opportunities. Moreover they are related to operationalization and
communicability.
Utility based measures analyze the economic benefits that people derive from access to the
spatially distributed activities. They interpret accessibility as the outcome of a set of transport
choices. Utility theory addresses the decision to purchase one discrete item from a set of
potential choices, all of which satisfy essentially the same need and can be used to model travel
behavior and the benefits of different users of a transport system. This type of measure has its
origin in economic studies.
2.3.2.2 CONVENTIONAL FORM OF ACCESSIBILITY MEASURES
Bhat (2002) made similar considerations about the measurement of accessibility filtering several
researches from the last few decades, five main types of measures have emerged. Each type of
19 2. Key Concepts
measure highlights a different way to characterize the interaction between the transportation
system and land use as well as a range of complexity.
The simplest accessibility measure is the distance or separation measure. The only dimension
used is distance, because these measures do not consider attraction level (e.g., land use), but
they are more than a mobility measure because they discount distances. The most general
network accessibility measure consists of the weighted average of the travel times to all the
other zones under consideration. The main criticisms to this kind of measure are several: the
lack of land use information, their reflexive nature (Pirie, 1979), the independence from land use
information (accessibility from point A to point B is the same as from point B to point A) and no
consideration of travel behavior.
The gravity measure includes an attraction factor as well as a separation factor. While the
cumulative-opportunities measure uses a discrete measure of time or distance and then counts
up attractions, gravity-based measures use a continuous measure that is then used to discount
opportunities with increasing time or distance from the origin. The general form of the model
has an attraction factor weighted by the travel time or distance raised to some exponent. The
cumulative-opportunities model is criticized for treating opportunities equally; including the
time or distance in the denominator of the equation, gravity-type measures provide a
dampening effect that devalues attractions far from the origin. Many researchers have explored
the appropriate nature of the impedance factor of the gravity equation. Some argue for a
Gaussian form that values nearby attractions highly and then falls off more quickly with
distance or time. Searching for an appropriate form and value of the impedance function, many
researchers find it appropriate to have different parameter values for different kinds of
attractions (many individuals are willing to travel farther for work than for other activities).
Another approach to measure accessibility is with a utility-based measure. This type of measure
is based on an individual’s perceived utility for different travel choices. The method of
calculating accessibility for an individual n, is the expected value of the maximum of the
utilities (Uin) over all alternative spatial destinations i in choice set C. Ben-Akiva and Lerman
(1979) proved that the utility form of accessibility meets several theoretical criteria as it does
not decrease with the addition of alternatives and it does not decrease if the mean of any one
choice utility increases.
Time-space measures add another dimension to the conceptual framework of accessibility
corresponding to the time constraints of individuals under consideration. The motivation
behind this approach to accessibility is that individuals have only limited time periods during
which to undertake activities. Constraints on time are generally divided into three classes
20 2. Key Concepts
(Hägerstrand 1970): capability constraints, related to the limits of human performance (e.g.,
people need to sleep every day); coupling constraints, when an individual needs to be at a
particular location at a particular time (e.g., work); and authority constraints, higher authorities
that inhibit movement or activities. The main criticism of space-time measures is that they are
difficult to aggregate because of their high level of disaggregation (Voges and Naudé 1983) and
it is difficult to look at the effects of changes on the larger scale such as in land use and the
transportation system.
2.3.2.3 IMPORTANCE OF PERSPECTIVES IN EVALUATING ACCESSIBILITY
Litman confirms (2017) that it is important to specify the perspective being considered when
evaluating accessibility. According to him accessibility can be viewed from various
perspectives, such as a particular person or group, mode, location or activity. For example, a
particular location may be very accessible to some modes and users, but not to others.
The first perspective to be considered is individuals and groups, specifying which users are
taken into account in the evaluation of transport. As a matter of fact, every different person and
group differs in accessibility needs and abilities (table 1) with consequent different problems to
be addressed.
Importance of
Transportation Modes
Groups
Walking Cycling Driving Public Transit Taxi Air Travel
Adult commuters 2 1 3 2 1 1
Business travelers 2 0 3 2 3 3
College students 3 3 2 2 0 1
Tourists 3 2 3 2 2 3
Low-income people 3 2 2 3 2 0
Children 3 3 2 1 0 1
People with disabilities 3 2 1 3 2 2
Freight delivery 0 1 3 0 1 1
TABLE 1: DIFFERENT GROUPS TEND TO RELY MORE ON CERTAIN MODES. RATING FROM 3 (MOST IMPORTANT) TO 0
(UNIMPORTANT) (LITMAN2017)
Basic accessibility analysis investigates people’s ability to reach goods and services considered
basic or essential, such as medical care, basic shopping, education, employment, and a certain
amount of social and recreational opportunities. This requires categorizing people according to
the following attributes. Vehicle accessibility, that is the degree that people have a motor vehicle
available for their use. Physical and communication ability and consideration of various types
of disabilities, including ambulatory, visual, auditory, inability to read. Income, in general
21 2. Key Concepts
people in the lowest income quintile can be considered poor. Commuting, the degree to which
people must travel regularly to school or work and dependencies, that is the degree to which
people care for children or dependent adults.
Another perspective considered by Litman is the mode of transport: different modes provide
different types of accessibility and have different requirements. For example, walking and
cycling provide more local access, while driving and public transit provide more regional
access.
Mode Speed User Cost User
Requirements
Facilities
Walking Low Low Physical ability Walkways
Cycling Medium Low Physical ability Paths/roads
Public Transit Medium Medium Minimal Roads/Rails
Intercity Bus and Rail High Medium Minimal Roads/Rails
Commercial Air Service Very High High Minimal Airports
Taxi High High Minimal Roadways
Private Automobile High High License Roadways
Ridesharing Moderate Low Minimal Roadways
Carsharing High High License Roadways
Telecommunications NA Varies Equipment Equipment
Delivery Services NA Medium Availability Roadways TABLE 2: COMPARISON OF TRANSPORTATION MODES (“TRANSPORT DIVERSITY,” VTPI, 2006)
A particular location’s accessibility can be evaluated based on distances and mobility options to
common destinations. For example, some areas are automobile-oriented, located on major
highways with abundant parking, poor pedestrian and transit access, and few nearby activities.
Other areas are transit-oriented, with high quality transit service, comfortable stations, good
walking conditions (since most transit trips include walking links), and nearby activities serving
transit users.
It is important to consider the types of activities involved, since certain types of users, travel
requirements, modes or locations affect their accessibility. For example, worksites with many
lower-income employees need walking, cycling, ridesharing and public transit access; industrial
and construction activities need freight vehicle access; hospitals need access for emergency
vehicles and numerous shift workers.
This summary shows how accessibility evaluation should consider various perspectives,
including different people, groups, modes, locations and activities. In other words, accessibility
should be sensitive to changes in the quality of transport service, the amount and distribution of
the supply of and demand for opportunities and temporal constraints, requiring separate
analysis for specific perspectives. In practical approaches it is difficult to apply all these set of
criteria since they involve a high level of complexity, different situations and study purposes
demand different approaches. However there are some basic and fundamental factors that
affect accessibility and they are presented in the next paragraph.
22 2. Key Concepts
2.3.3 FACTORS
In this paragraph a list of specific factors that affect accessibility is presented and the degree to
which they are considered in current transport planning (Litman, 2017).
2.3.3.1 TRANSPORTATION DEMAND AND ACTIVITY
Transportation demand refers to the amount of mobility and accessibility people would
consume under various conditions. Transportation activity refers to the amount of mobility and
accessibility people actually experience. Travel demand can be categorized in various ways
according to demographics (age, income, employment status, gender etc.), purpose
(commuting, personal errands, recreation, etc.), destination (school, jobs, stores, restaurants,
parks, friends, families etc.), time (hour, day, season), mode (walking, cycling, automobile
driver, automobile passenger, transit passenger, etc.) and distance. Demographic and
geographic factors affect demand both for mobility and access; for example, attending school,
being employed or having dependents increases demand. Price, quality and other factors affect
demand for each mode and therefore mode split.
2.3.3.2 MOBILITY
Mobility refers to physical movement, measured by trips, distance and speed, such as person
miles or kilometers for personal travel and ton miles or ton kilometers for freight travel. For
example, considering all else being equal, increased mobility increases accessibility: the more
and faster people can travel the more destinations they can reach. However, many times an
increasing in mobility does not correspond to an accessibility improvement. As explained in the
previous chapter, conventional planning tends to evaluate transport system quality primarily
based on mobility, using indicators such as average traffic speed and congestion delay. That is
the crucial point, efforts to increase vehicle traffic speeds and volumes can reduce other forms
of accessibility, by contrasting pedestrian travel and stimulating more automobile oriented
development patterns. Moreover higher occupancy modes can increase personal mobility
without increasing vehicle travel: improving high occupant vehicle (HOV) travel and favor it
over driving can reduce congestion and increase personal mobility (person-miles of travel)
without increasing vehicle mobility (vehicle-miles of travel).
23 2. Key Concepts
2.3.3.3 TRANSPORTATION MODES
Transportation options refer to the quantity and quality of transport modes and services
available in a particular situation. In general, improving transport options improves
accessibility. Modes differ in their capabilities and limitations and so they are most appropriate
for serving different demands, different types of users and trips. For example, active modes
(walking and cycling) are most appropriate for shorter trips, public transit is most appropriate
for longer trips on major urban corridors, and automobiles are most appropriate for trips that
involve heavier loads, longer trips and dispersed destinations. The quality of different modes
can be evaluated using various level-of-service (LOS) ratings, which grade service quality from
A (best) to F (worst). Conventional planning tends to evaluate transport system quality based
primarily on automobile travel conditions, but similar ratings can be applied to other modes, as
indicated in Table 3 (Litman 2007b).
Mode Level of Service Factors
Universal design (disability access) Degree to which transport facilities and services
accommodate people with disabilities and other special
needs.
Walking Sidewalk/path quality, street crossing conditions, land use
conditions, security, prestige.
Cycling Path quality, street riding conditions, parking conditions,
security.
Ridesharing Ridematching services, chances of finding rideshare
matches, HOV priority.
Public transit Service coverage, frequency, speed (particularly compared
with driving), vehicle and waiting area comfort, user
information, price, security, prestige.
Automobile Speed, congestion delay, roadway conditions, parking
convenience, safety.
Telework Employer acceptance/support of telecommuting, Internet
access.
Delivery services Coverage, speed, convenience, affordability.
TABLE 3 MULTI-MODAL LEVEL OF SERVICE (“TRANSPORT OPTIONS,” VTPI 2006; FDOT 2007)
2.3.3.4 INFORMATION PROVIDED TO USER
Another important factor that affects accessibility is the information provided to the user. The
quality of information can affect the functional availability and desirability of mobility and
accessibility options. Again, the kind of information given is different and has different
meanings according to the mode of transport is being considered. Motorists need information
about travel routes, congestions, accidents, parking availability. Transit users need information
on transit routes, schedules, delays and access to destination. Finally, walkers and cyclists need
information on recommended routes with path walk and cyclist need information on parking
option. Nowadays the way to provide transportation has become more efficient compared to
last decades, thanks to new communication systems including in-vehicle navigation systems for
24 2. Key Concepts
motorists, websites with route and schedule information and real time information on transit
delays.
2.3.3.5 INTEGRATION AMONG MODES
Accessibility is also affected by the quality of system integration, such as the ease of transferring
between modes, the quality of stations and terminals, and parking convenience. Critical matter
is the transfer between different modes, this does not only refer to the position of the stops or
stations, but also to their comfort and qualities. Of course their evaluation changes with the
typology of stops, for example for pedestrians is fundamental to have easy access to stops,
comfortable waiting area, in particular for people with disabilities, children, and people
carrying heavy loads.
2.3.3.6 LAND USE FACTORS
As already mentioned in the previous chapters, land use has a high impact on accessibility,
including density mix and connectivity. Basically a more accessible land use pattern means that
less mobility is required to reach the destination. Consequently travel distances and options
among these destinations affect overall accessibility, that is the reason why improving the
variety of services (shops, schools, restaurants, parks, etc.) within the same area tends to
increase accessibility and reduce transport expenditures. It is important to distinguish two
concepts: density and clustering. The first refers to the number of people or jobs per acre while
the second refers to people and activities locating together. Low-density areas can have a high
degree of clustering, such as rural residents and businesses locating in villages. Land use mix
refers to various land uses (residential, commercial, institutional, recreational, etc.) located close
together, but this aspect will be studied in deep in the next chapters. Anyways, the relationship
between density and accessibility is complex, the connection between the concepts is not direct.
For example, increased density and clustering can increase traffic and parking congestion for
motorized users, with consequent reduction of accessibility. Other modes, such as walking and
public transit, require less space and benefit from density. Clustering activities into a compact
area makes it feasible to perform numerous activities with one trip, which is helpful to
motorists and even more to transit users
2.3.3.7 CONNECTIVITY
Connectivity is a measure of the quantity of the connections in the network and thus the
directness and multiplicity of routes through the network (Tal, 2012). Increased connectivity
25 2. Key Concepts
tends to increase accessibility (Litman, 2017). Dill (2004) examined common measures of
connectivity for bicycling and walking and defined a connectivity index taking into account
several characteristics of road pattern.
Block length is used in a number of ways to promote or measure connectivity (Cervero, 1997).
Standards usually range from 300 to 600 feet and apply to every block with some exceptions, for
walking and pedestrian environment shorter distances are recommended. Block lengths are
measured from the curb or from the centerline of the street intersection. The concept is that
shorter blocks mean more intersections, shorter travel distances and a greater number of routes
between locations.
Another standard to evaluate connectivity is setting the maximum block sizes (Hess, 1997),
which capture two dimensions of the block. These dimensions are usually the width and the
length as to calculate the area or the perimeter, then using block size as a standard may be more
flexible than block length. By the way, it still has some drawbacks: the impact on walking and
cycling distances between two points is unclear. Consider the two simple examples in Figure 2.
Under Plan A, each block face is the same length. In Plan B, the same four blocks are half as
wide, but twice as long. The perimeters and areas of the blocks are the same in each plan. The
walking distance between points A and B, located on opposite sides of the development, for
Plan A is shorter than Plan B. But, when the two points are located on the same block, near one
end, the distance for Plan B is shorter.
FIGURE 3: MAXIMUM BLOCK LENGTH VS. BLOCK SIZE
Block density has been used as a measure for connectivity. Some researchers (Frank et al., 2000)
used the mean number of census blocks per square mile, since census blocks are usually defined
as the smallest fully enclosed polygon bounded by features as roads on all sides. Others
(Cervero, 1997) used blocks defined more traditionally, areas of land surrounded by streets.
Anyway in both cases, since more blocks means smaller blocks so more intersections, increasing
block density suggests increasing connectivity.
26 2. Key Concepts
In fact, intersection density is measured as the number of intersections per unit of area, e.g.
square mile. A higher number would indicate more intersections, which means more path
choices and higher connectivity.
Street density is measured as the number of linear miles of streets per square mile of land (or
kilometers per square kilometer). A higher number would indicate more streets and,
presumably, higher connectivity. Street density, intersection density, and block density are
likely highly and positively correlated with each other (Handy, 1996).
The Connected Node Ratio (CNR) is the number of street intersections divided by the number
of intersections plus cul-de-sacs (Allen, 1997). The maximum value is 1.0. Higher numbers
indicate that there are relatively few cul-de-sacs and a higher level of connectivity.
Link-Node Ratio is an index of connectivity equal to the number of links divided by the number
of nodes within a study area. Links are defined as roadway or pathway segments between two
nodes. Nodes are intersections or the end of a cul-de-sac. Theoretically increased LNR means
increased connectivity and a perfect grid has a ratio of 2.5 (Ewing, 1996). Figure 3 shows an
example of two different situations. Both plans have the same number of nodes. Plan B has two
additional links, resulting in a link-node ratio of 1.13 versus 0.88 for Plan A. Under Plan A there
is only one route between points A and B. Under Plan B there are three potential routes.
Anyways, link-node ratio does not reflect the length of the links, that is an important point
especially for walking and biking users. In addition, link-node ratio is less intuitive and,
therefore, may be less attractive as a policy tool.
FIGURE 4: LINK NODE RATIO
As already deeply explained the street pattern plays a fundamental role in the road
connectivity, basically the more it is covered by a grid pattern, the more connected it is. For
example, Boarnet and Crane (2001) use the percentage of area in a one-quarter mile buffer zone
that is covered by a grid street pattern, as measured by four-way intersections. Boarnet and
27 2. Key Concepts
Crane chose this measure based upon research that showed that the number of four-way
intersections was a good predictor of whether a neighborhood reflected "neotraditional" design
elements.
Since distance traveled for a trip is a primary factor in determining whether a person walks or
bikes, Pedestrian Route Directness reflects this factor. It is the ratio of route distance to straight-
line distance for two selected points, the lowest possible value is 1.00, where the route is the
same distance as the "crow flies" distance. Numbers closer to 1.00 therefore indicate a more
direct route, theoretically representing a more connected network.
All these characteristics show how the concept of connectivity is strictly related to accessibility.
However, as already explained, for a correct analysis the connectivity should be taken into
account from the different perspectives introduced in the previous paragraph. For example for
pedestrians, connectivity is an indicator of how accessible, with regards to walking, a
neighborhood is to its residents. Residents desire to walk to local destinations, such as schools,
community centers, transit stops, or shopping. Various factors influence an individual’s
decision to walk rather than drive for an origin-destination trip and the most important include
the availability of a local destination (implying some mixture of land uses), personal health and
fitness, route distance, and route directness.
2.4 EQUITY
2.4.1 DEFINITION
Accessibility is a measure of potential opportunities (Hansen, 1959). Access to opportunities
such as jobs and services is one of the main benefits of a transportation service such as public
transit. Then accessibility has important social and equity impacts. The quality of a person or
group’s access determines their opportunity to engage in economic and social activities
(Litman, 2017). Accessibility measures explained in the previous paragraph are seen as
indicators for the impact of land use and transport developments and policy plans on the
functioning of society in general. In fact accessibility’s impact on land use and transport system
gives individuals or group of individuals the opportunity to participate in activities in different
locations (Geurs, 2003). Intuitively, low-income and socially disadvantaged individuals are the
most likely to be affected by inequality as they are usually transit dependent, and often face
greater barriers to access their desired destinations, both spatially and economic barriers.
Anyway a lot of types of users are involved in this matter, attesting the importance of correctly
evaluating the accessibility of an urban area. A significant portion of people could or could not
drive because they lack a driver’s license, have a disability, cannot afford a car, are impaired by
28 2. Key Concepts
alcohol or drugs, or prefer to use alternative modes in order to save money, reduce stress, or
exercise more.
Transportation planning decisions can have significant and diverse equity impacts (Litman,
2017):
The quality of transportation available affects people’s economic and social
opportunities.
Transport facilities, activities and services impose various indirect and external costs,
such as congestion delay and accident risk imposed on other road users, infrastructure
costs not funded through user fees, pollution, and undesirable land use impacts.
Transport expenditures represent a major share of most household, business and
government expenditures.
Transport facilities require significant public resources (tax funding and road rights of
way), the allocation of which can favor some people over others.
Transport planning decisions can affect development location and type, and therefore
accessibility, land values and local economic activity.
Transport planning decisions can affect employment and economic development which
have distributional impacts.
2.4.2 TYPOLOGIES
There are three types of equity: horizontal equity, vertical equity with regard to income and
social class, vertical equity with regard to mobility need and ability.
Horizontal equity refers to the uniform distribution of benefits and costs among individuals
within a group. Based on egalitarian theories, it avoids favoring one individual or group over
another. Most studies of horizontal equity look into spatial distribution of transportation
impacts. However, with regard to public transit, some groups are more likely require such
service, namely low-income populations that are transit-dependent as they cannot afford
owning a car (El-Geneidy, 2016). It means that public policies should avoid favoring one
individual or group over others, and that consumers should “get what they pay for and pay for
what they get” from fees and taxes unless subsidies are specifically justified (Litman, 2017).
Vertical equity with regard to income and social class considers the distribution of benefits
between groups, and compares the benefits across socio-economic groups. Transport policies
are equitable if they favor economically and socially disadvantaged groups in order to
compensating for overall inequities (Rawls 1971). Policies are called progressive if they favor
disadvantaged groups and regressive if they harm such groups. In the case of transportation,
29 2. Key Concepts
potentially disadvantaged populations include low-income and unemployed people as well as
minorities. There is general agreement that everybody deserves equity of opportunity, meaning
that disadvantaged people have adequate access to education and employment opportunities.
There is less agreement concerning equity of outcome, meaning that society insures that
disadvantaged people actually succeed in these activities.
Vertical equity with regard to mobility need and ability concerns with the distribution of
impacts between individuals and groups that differ in mobility ability and need, and therefore
the degree to which the transportation system meets the needs of travelers with mobility
impairments. This definition is used to support universal design, which means that transport
facilities and services accommodate all users, including those with special needs (Litman, 2017).
These different types of equity often overlap or conflict. For example, horizontal equity requires
that users bear the costs of their transport facilities and services, but vertical equity often
requires subsidies for disadvantaged people. Therefore, transport planning often involves
making tradeoffs between different equity objectives.
2.4.3 EVALUATION
Transportation analysis is affected by how transport is defined and evaluated (CTS 2006).
Conventional planning is based on mobility, that means the physical movement, using
indicators such as traffic speed or roadway level of service. However, the ultimate step to
permit movement is providing access, referring to people’s ability to reach desired services and
activities. Anyway mobility based planning has important equity implications. Since the most
used indicators refers to traffic speed and road LOS, it tends to favor faster modes and longer
trips over slower modes and shorter trips, so motorist over non-drivers. This kind of analysis
and planning justifies somehow roadway expansion projects building wider roads and
increasing traffic speeds, which are the most affecting barriers to walking and cycling activity.
As already said, walking is the first and last step of every trip and plays a fundamental role in
providing access, particularly transit access. Accessibility based evaluation can consider such
tradeoffs and their equity impacts, it expands the range of impacts and options considered in
planning. Such evaluation recognizes the important roles that active and public transport can
play in an efficient and equitable transport system. It considers impacts such as the barrier effect
and sprawled development on accessibility, and expands transport improvement options to
include improvements to alternative modes, increased transport network connectivity, more
accessible land use development, and improved telecommunications and delivery services. This
provides more comprehensive equity evaluation (Litman, 2017).
30 2. Key Concepts
It is fundamental to understand the assumptions and perspectives of different measurement
units in considering the analysis of equity impact. As for mobility based analysis, transportation
activities and impacts can be measured in various ways according to the goal of the study,
affecting the results. For an equity analysis, usually impacts are compared using reference units:
per capita assumes that every person should receive an equal share of resources, per trip
assumes that people who travel more should receive more resources, per cost assumes that
people should receive public resources in proportion to how much they pay in fees and taxes.
Another important matter to consider in the equity analysis is categorizing people. It is not
straight forward to identify disadvantaged people in a community. Disadvantaged status is
multi-dimensional, so its evaluation should take into account the degree and number of
disadvantaged factors that apply to an individual. In general, equity evaluation requires that
people be categorized by demographic and geographic factors to evaluate their capabilities and
identify who are transport. From a transportation point of view, people are often categorized as
motorists, transit users and pedestrians. Some studies (Fan and Huang, 2011; Karner and
Niemeier, 2013) revealed that only a small portion of households depend completely on public
transit at any time, many have members who use transit, and many people who do not
currently use it may value having it available for possible future use. Why do low-income
households choose to own vehicles even though that ownership brings additional hardship?
The answers to this question are rooted in the complex transportation needs of low-income
households, needs often determined not only by movement utility but mostly by household
structure. For example, a household with multiple workers or with children, all else equal, is
more likely to own a car and for this reason, it is often most appropriate to use a household or
lifecycle analysis for equity analysis (Ryan, 1999). The table below lists the factors which can
contribute to transportation disadvantaged status, the greater their degree of disadvantaged
and the more factors that apply, the more disadvantaged an individual or group can be
considered.
FACTORS
Low Income Non-driver/car-less Disability
Language Barriers Caregiver Obligations
TABLE 4: FACTORS CONTRIBUTING TO TRANSPORTATION DISADVANTAGED STATUS
31 2. Key Concepts
2.5 TRANSIT ORIENTED DEVELOPMENT
2.5.1 CONCEPT DELINEATION
The last fundamental concept to be presented involves all the theoretical arguments explained
above, as it represents an integrated approach to transportation and land use planning: transit
oriented development. Transit Oriented Development (TOD) refers to residential and
commercial centers designed to maximize access by transit and nonmotorized transportation,
and with other features to encourage transit ridership (Litman, 2017). A typical TOD has a rail
or bus station at its center, surrounded by relatively high-density development, with
progressively lower-density spreading outwards one-quarter to one-half mile, which represents
pedestrian scale distances (Renne 2009). It can do more than simply shift some car trips to
transit: it also increases accessibility and transportation options through land use clustering and
mix, and nonmotorized transportation improvements. This reduces the distance required for
car trips, allows a greater portion of trips to be made by walking and cycling, and allows some
households to reduce their car ownership, which together can result in large reductions in
vehicle travel. The benefits of TOD provides an array of benefits ranging from lifestyle to
environmental to economic (The Transit Oriented Development Institute):
reduce dependence of driving;
allow residents to live, work and play in the same area;
reduce the area’s carbon footprint or negative impact on the environment;
provide access to better life services;
stimulate the local economy;
provide better access between urban and suburban areas;
provide access to better entertainment or recreational services;
provide access to better jobs;
revitalize urban areas.
Effective TOD depends on various factors, including higher than average density, land use mix,
roadway connectivity and design and also building design. Renne (2009) defines specific factors
required for true Transit-Oriented Development, so residents own fewer cars, drive less, rely
more on alternative modes and have a high level of local accessibility, as opposed to Transit
Adjacent Development, which is conventional, automobile-oriented development located near
transit stations (Table 5). Pollack, Gartsman and Wood (2013) developed the eTOD station area
rating system which evaluates specific rail stations based on the quality of transit service, rider
orientation and the connectivity of local development to the station.
32 2. Key Concepts
Transit Oriented Development Transit Adjacent Development
Grid street pattern Suburban street pattern
Higher densities Lower densities
Limited surface parking and efficient parking
management
Dominance of surface parking
Pedestrian and bicycle–oriented design Limited pedestrian and cycling access
Mixed housing types, including multi-family Mainly single-family homes
Horizontal (side-by-side) and vertical (within the same
building) mixed use
Segregated land uses
Office and retail, particularly on main streets Gas stations, car dealerships, drive-through stores and
other automobile-focused land uses
TABLE 5: TRANSIT ORIENTED VERSUS ADJACENT (RENNE 2009)
Krizek’s study (2003) analyzes the travel behavior of the same households in a longitudinal
manner in concert with detailed urban form measures. The findings suggest that households
change travel behavior when exposed to differing urban forms. Locating to areas with higher
neighborhood accessibility decreases vehicle miles traveled, while people who live in areas with
good accessibility are more likely to walk and use transit than those who live in more
traditional automobile-oriented environments.
An often unspoken but key component to TOD theory is pedestrian access between the transit
stop and the immediately surrounding area (Schlossberg, 2004). That is, the success of TOD
significantly rests on the capacity of pedestrians to navigate and access the range of land uses in
close proximity to transit stations. A core component of TOD success also rests in the capacity of
transit users to access the transit stop to begin with or to access key destinations after reaching
their destinations. Thus, the pedestrian environment surrounding transit stops is a key element
in understanding TOD because transit riders are pedestrians on at least one end of their transit
trips (City of Portland Office of Transportation).
2.5.2 SERVICE AREA
Transit stops and in particular bus stops are crucial elements in designing since they meet the
requirement of different environments and affect the accessibility of the transit usage itself. A
lot of factors have to be taken into account in designing phase, as to provide different solutions
to meet a number of common requirements and functions, from the comfort while waiting,
boarding and alighting to getting information about the service. Moreover bus journeys occur
under different circumstances (whether conditions, peak times, assistance requirements etc) and
environment (urban or rural, outdoor or indoor etc). Thus, the bus stop must be conceived as an
activity system in which the main function is strictly interrelated with additional or associated
activities occurring in the surroundings. The attention then should comprehend the
characteristics of the stops themselves but also the service area.
33 2. Key Concepts
However the delineation and the definition of the service area it is not that simple. A service
area around a transit station or stop is broadly defined as the area from which potential riders
are drawn. Many transit planners and engineers depend on simplified methods when
determining service areas around transit stations especially in regard to walking. A 400 meters
buffer (0.25 miles) is commonly defined around bus stops (Zhao, 2003) and an 800 meters buffer
(0.5 miles) is commonly used for rail stations (Schlossberg, 2007) as the areas from which most
users accessing the system by foot originate (figure 4). In Toronto, Canada, Alshalalfah (2007)
showed that among transit users, 60% live within 300 meters from their stop and 80% within
500 meters. Anyway it is clear from the results of several studies that these distances are
different between them and they are significantly beyond the 400 meters for buses and 500
meters for rail. These differences reflect variations between sections in the regions where data
were collected as well as variations between regions. Accordingly, service areas around transit
stations should vary according to the service being offered and the location in the region.
FIGURE 5: DISTANCE DECAY TO METRO, TRAIN AND BUS SERVICES (EL-GENEIDY, 2013)
The first element to consider when analyzing walking distances to stops is that pedestrians first
and foremost seek to minimize both the distance and the time of the walking portion of their
trip (Schlossberg, 2007). After that, individual characteristics, station and area characteristics,
transit route features and weather temperature can have an effect on walking distances. Higher
household incomes negatively affect propensity to walk while higher population and dwelling
density and education have positive effects, although not necessarily on distances of those who
do walk. Higher vehicle availability relates negatively to walking likelihood but positively to
walking distance, presumably because car-owning households locate with less emphasis on
34 2. Key Concepts
transit access (Alshalalfah, 2007). Area characteristics favoring pedestrian access include the
absence of barriers, a grid street pattern providing for more pedestrian linkages, higher
densities, land use mix, a small number of parking spaces at the station, safety and an attractive
and reliable transit service (Zhao, 2003). In terms of transit stops, a higher number of transit
lines at a stop or station increases the willingness to walk, while longer waiting time and higher
number of transfers during a trip decrease access walking distances. One direct service quality
measure that was found significant, if here only at the 90% confidence level, is wait time: for
each additional minute of wait time, users walk on average a little over 2 meters less,
suggesting wider appeal of more frequent buses or of many lines stops (El-Geneidy, 2013).
Another issue is how to practically measure the service areas around transit facilities. Coverage
or service areas can be delineated by GIS through the creation of buffers (bands) around transit
facilities based on Euclidean (straight-line) distance. A second method of operationalizing
service areas in GIS is based on calculations of distances or travel times along a street network
(network distance) (figure 5). The choice of the distance calculation method affects significantly
the final results. Buffer areas include streets that are inaccessible to the transit stops because of
the characteristics of the street network. Given a distance threshold (for example, 0.25 miles),
service areas are wider using Euclidean distances than network distances, such that the first
method overestimates the size and the population of the service areas (Gutierrez, 2008).
FIGURE 6: CALCULATION OF SERVICE AREAS IN A STRAIGHT LINE (CIRCLE), AND THROUGH THE STREET NETWORK
(GUTIERREZ, 2008)
Finally, it is clear the importance in the area of transit oriented development of identifying the
exact service are around transit stations as walking distances vary based on neighborhood,
35 2. Key Concepts
household, personal trip and route characteristics: people walk longer distances to routes with
shorter wait time and according to transit types (figure 4).
36 3. Thesis Purpose and Methodology
3. THESIS PURPOSE AND METHODOLOGY
3.1 AIM OF THE WORK
The aim of the work is to evaluate the pedestrian accessibility to bus and tram stops of a district
in Rome, using an integrated approach that touches all the fundamental concepts expressed in
the previous chapter. Thus, this analysis wants to explore the relation between pedestrian
environment, land use and transit characteristics, through a methodology that takes into
account different kind of indicators describing different design perspectives. One of the
strongest points of the work is its sensitive to changes, adapting the weight of the factors with
respects to the specific project or typology of user.
As deeply explained in the previous chapter, in the last two decades the importance of
pedestrian environment and the design of efficient public transport services have been
increasing as they reflect a fundamental role in the development of the sustainable city. That is
also the reason why this work wants to gather these two concepts analyzing their relation
represented by the accessibility of the bus stops.
Accessibility means providing access to some services, so in this case the goal is to evaluate the
access to the public transport service. Then the bus stops are at the same time the end point of
the pedestrian trip, thus representing a service itself, and the starting point for the public
transport trip, providing the access to another service. So the transit stops play a fundamental
role in the designing of the sustainable city, since they are the connecting point between
pedestrian and transit environments. They basically consider pedestrian needs over motor
vehicle ones, so the evaluation of their accessibility should take into account the concepts of
walkability, urban planning, equity and connectivity.
As a matter of fact, the ideas of land usage, transport design, users’ satisfaction and urban
planning should not be considered separately, since every element is interconnected with the
other. This work tries to consider indicators that range over different fields, as to englobe the
most possible characteristic that influence the choice and the satisfaction of the users. Moreover
it is possible to adapt it according to the specific goal, giving more importance to the specific
interested field, from the user comfort, to the efficiency of transit service or to the urban
connectivity. Here is where the project touches the concept of equity, fundamental for a
sustainable development according to European polices: not only because transit can give to
individuals or group of people who do not have the driver’s license the opportunity to
participate in activities in different locations, but, giving more importance to some indicators as
37 3. Thesis Purpose and Methodology
comfort or level of service of the bus stop, the project can also focus on the needs of that portion
of people which are affected by physical disabilities.
3.2 CASE STUDY: NOMENTANO DISTRICT IN ROME
3.2.1 BRIEF PRESENTATION OF THE NEIGHBORHOOD
Nomentano is the name of the fifth neighborhood of Rome, usually indicated as Q.V.. The name
also refers to the urbanistic zone 3A, being part of the Municipio Roma II. It is situated in the
north-east part of the city, close to ancient Aurelian walls. Its shape is similar to an irregular
triangle and it is bounded by neighborhoods Salario and Trieste at north, by Pietralata at east,
by Tiburtino at south and by Castro Pretorio at south-west.
FIGURE 7: LOCATION OF NOMENTANO DISTRICT
Nomentano district was born in 1911 and it has been officially instituted in 1921, as it is one of
the oldest neighborhoods of Rome. Its name originates from the roman Via Nomentana, an
ancient road of Italy, leading North-East from Rome to Nomentum (modern Mentana).
The district develops along via Nomentana until the railway overpass that delimits it by the
eastern side. It is crossed by two main roads: viale Regina Margherita – viale Regina Elena,
linking via dei Parioli with piazzale del Verano, and viale del Policlinico – via G.B.Morgagni –
Via della Lega Lombarda, linking via Tiburtina with Porta Pia and Corso d’Italia.
38 3. Thesis Purpose and Methodology
FIGURE 8: POPULATION OF DISTRICT ZONE 3A IN 2016, BY FIVE-YEARS AGE GROUPS
Its area is of 32611 km², with 39245 inhabitants registered in 2016 census for a density
population of 12034,28 inhab/km².
From the census of 2016 it has been revealed that the district has an old-age index a bit higher
with respect to the rest of the city (figure 7), probably due to its residential nature. There are a
lot of buildings dedicated to tertiary sector activities, not only commercial (figure 8), but also
public services like schools, hospitals, churches and parks.
0
500
1.000
1.500
2.000
2.500
3.000
3.500
0-4
5-9
10-1
4
15-1
9
20-2
4
25-2
9
30-3
4
35-3
9
40-4
4
45-4
9
50-5
4
55-5
9
60-6
4
65-6
9
70-7
4
75-7
9
80-8
4
ov
er 8
5
Population of district zone 3a in 2016 by five-years age
groups
39 3. Thesis Purpose and Methodology
FIGURE 9: COMERCIAL ACTIVITIES DISTRIBUTION IN NOMENTANO DISTRICT
Due to this variety of activities and its position with respect to the city centers, the district is
strongly affected by traffic and high levels of air pollution, both higher than the average of the
municipality of Rome.
All these factors bring the attention to a development of the walking environment and the
transit services, providing access to activities located in the neighborhood to older people, but
also to the close “La Sapienza” university for younger ones.
3.2.2 DESCRIPTION OF THE ANALYZED AREA
The service area taken into account for the study of the pedestrian accessibility to bus stop does
not correspond precisely with the district boundaries. In fact, as already deeply explained in the
paragraph 2.5.2, conventionally the pedestrian is considered willing to walk 400 meters to reach
the bus stop, thus the area considered for the project has been extended through the Buffer tool
using the Arcgis software, as shown in the figure 9.
40 3. Thesis Purpose and Methodology
FIGURE 10: BUFFER PROCESS OF NOMENTANO DISTRICT
Therefore the extended area covers more bus and tram stops which, according to the hypothesis
explained above, can be reached by the inhabitants from the Nomentano district. However, for
a more complete analysis, all the building, streets, transit lines, attractive points and inhabitants
within the new area have been taken into account for the study.
Accordingly to this, the total number of bus and tram stops is 231. It must be specified that
metro and train lines and stations have not been considered in the work, because they would
request specific indicators, since they provide a bit different service and satisfy a larger number
of users’ needs.
The list of all the stops is provided in appendix B. The data about the position and the number
of transit stops have been downloaded from the site of RomaMobilità, which provides some
dataset for transit characteristics.
41 3. Thesis Purpose and Methodology
FIGURE 11: DISTRIBUTION OF BUS AND TRAM STOPS IN NOMENTANO AREA
The picture above shows how the distribution of the bus stops is well spread over the
neighborhood. In particular, it can be seen how the bus stops are situated along the major roads
Via Nomentana - viale Regina Margherita - viale Regina Elena, Via Giovanni Battista Morgagni
- via Bari - via Catania - via della Lega Lombarda and Corso Trieste - Viale Eritrea. More over
the density of the stops is higher in the areas close to the metro or rail stations, as in Piazza
Bologna, Stazione Tiburtina, Castro Pretorio and S.Agnese – Annibaliano. Another interesting
case is represented by Piazzale del Verano, where tram and bus are interconnected and
furthermore it is close to some attractive buildings as the University “´La Sapienza” or the
cemetery of Rome.
The area is crossed by 45 different lines, two of them are tram lines: line number 3 and line
number 19. Night lines and dedicated lines have not been considered. The list of the lines is
again provided in appendix A. The data about the transit lines have been also downloaded
from the site of RomaMobilità, weekly uploaded. The picture 11 below shows that the area is
mostly served by bus lines, while tram ones just cross the district in the southern part along
Viale Regina Margherita – Viale Regina Elena, beside the University area, providing access to a
focal attracting point as school. From the picture, it could seem that some area are not reached
by public transport, however it should considered that the neighborhood is provided by some
public services as the Policlinico Umberto I, in the university area in the southern part, the
42 3. Thesis Purpose and Methodology
cimitery mentioned above and some parks and villas where people can spend their free time,
for example Villa Torlonia, Villa Massimo, Villa Leopardi or Villa Blanc.
FIGURE 12: BUS LINES (GREEN) AND TRAM LINES (RED) CROSSING NOMENTANO DISTRICT
3.3 METHODOLOGY
The analysis methodology wants to retrace the concepts expressed in the second chapter. Thus
it starts with the examination of the structure of the district, as to introduce some basic
indicators in order to understand the fundamental skeleton of the neighborhood and its
connectivity. Then the attention moves to the final goal of the entire work, evaluating 7
indicators for each one of the 231 stops of the district as to calculate their accessibility: number
of lines, frequency, land-use entropy, level of service, pedestrian catchment area, inhabitants
served and comfort. Finally a multicriteria analysis is developed in order to find a final value
that can express the accessibility of the stop. The Ideal Point Method has been used and the
indicators are weighted using the Pairwise Comparison Method. More over the indicators in the
PCM are rated using the results of a survey collecting the opinions of Master students, PhD
students or professors expert in transportation field, as to make the analysis the most objective
possible.
In the next paragraphs the indicators describing the structure of the neighborhood and the ones
describing the accessibility of the stops are presented, then the two method used for the
43 3. Thesis Purpose and Methodology
multicriteria analysis are explained. Finally, the idea behind the choice of using a survey and its
structure are cleared.
FIGURE 13: CONCEPTUAL MAP
3.3.1 STREET NETWORK ANALYSIS
The road network represents the basic skeleton of the urban form, creating the range of
opportunities and path choice that can make walking more or less desirable. There are other
ways to identify walkable routes, including sidewalks and off-street paths, but for many
environments sidewalks and streets are synonymous and off-streets path are rare (Schlossberg,
2006). The urban form around key places of interest is important for increased pedestrian access
and activity, and the street network often acts as the skeleton for this urban form.
Three scales of indicators are presented and analyzed for evaluating the structure of the
neighborhood in relation with its walkability and connectivity. Even if these features are not
used for the calculations of the final goal of the study, they are useful to have a preliminary
44 3. Thesis Purpose and Methodology
quick view of the structure of the district and to point out which areas are more pedestrian
friendly and which ones are more automobile oriented.
3.3.1.1 STREET CLASSIFICATION
Currently, streets are categorized in a hierarchical, automobile-centered manner ranging from
arterial to collector or feeder roads, implying that all roads serve the singular purpose of
automobile mobility. Street classification analysis is an evaluation and categorization of street
type and purpose along the road network. Areas with high automobile speeds or large volumes
of traffic are characteristics of locations hostile to pedestrians, they present impedances to
pedestrians because the scale and feel of such roads affect negatively the ability or desire of
users to cross or walk along them. The streets classification analysis addresses the request to
access road functionality, defining the relationship between impedance roads, hostile to
pedestrians, and accessible roads.
Minor roads are generally more walkable because of decreased speeds and automobile volume,
therefore areas with large number of minor roads may indicate a more walkable area than an
area with fewer minor roads.
On the contrary, major roads often act as impediments for pedestrians who have to walk along
them or cross them to access a destination, therefore the greater the number of major roads, the
worse the walking environment may be.
So, areas with high density of minor roads may offer more pedestrian route options than areas
with lower densities. However it is possible that the presence of major roads can offset the
benefit of a large number of minor roads, especially if the major roads are located central to the
walkable area of interest.
However Italian classification of urban roads is a bit more complicated as it mainly involves
four typologies of roads according to their functional characteristics.
Autostrade and links (type A): their function is to provide entrance and exit to/from the
city. They link urban and extra urban environments;
Strade urbane di scorrimento (type D): they guarantee the quality and ease of movement
within the urban network providing an high level of service to long distance
movements;
Strade urbane di quartiere (type E): they link close districts, or for the larger areas, they
connect two extreme zones of the same neighborhood. The movements are shorter with
respect to the ones served by type D roads;
45 3. Thesis Purpose and Methodology
Strade locali (type F): their function is to guarantee pedestrian movements and the direct
access to the building, thus they serve the first and final part of private vehicles
movements.
Moreover some other typologies of roads have been introduced, characterized by halfway
characteristics, in order to adapt their functional and physical features:
Strade urbane di scorrimento veloce (type A), they have intermediate characteristics
between type A and type D roads;
Strade urbane interquartiere (type D), they have intermediate characteristics between type
D and type E roads;
Strade interzonali (type E), they have intermediate characteristics between type E and
type F roads.
As said before, identifying the roads with their functional classification can help to understand
immediately the pedestrian friendliness of the urban environment. A map will be provided as
to give an immediate graphical impact, similar to the example below (figure 13).
FIGURE 14: HIERARCHICAL CHARACTERISTICS AND FUNCTIONAL LINKS OF URBAN ROADS
(REGOLAMENTO VIARIO 2015)
3.3.1.2 INTERSECTION DENSITY
The intersection intensity examines the street network within the analysis area based on the
spatial location of certain types of intersections to capture the grain and the connectivity of a
neighborhood. Intersections represent the number of choices available to a pedestrian and, from
a spatial perspective, how these choices are arranged within the study zone. Thus, areas that are
more walkable would tend to have higher intersection densities and lower dead-end densities.
One would expect that areas with more roads would have more intersections, anyway this
relation is not so immediate. Analyzing independently intersection densities is important
because it gives insight into connectedness of the mobility network that might not be evident
from simply looking at the length of the network. The ratio between intersections and dead
46 3. Thesis Purpose and Methodology
ends is another useful way to understand the mobility infrastructure, because path continuity is
important and the higher the ratio, the fewer potential barriers there are for walkers. From a
review of literature, it has been found that walkable areas are characterized by minimum
intersection densities of 100 intersections per mi², with areas exceeding 150 intersections per mi²
being highly walkable (Schlossber, 2006).
For these reasons, the following indicators will be provided in order to understand the urban
environment and road network and its relation with pedestrians: intersection density, dead-end
density and intersection-dead end ratio.
3.3.1.3 PEDESTRIAN CATCHMENT AREA AND NETWORK CONNECTIVITY
Pedestrian catchment areas (PCA) are theoretically walkable zones that can be mapped to show
the actual area that can be accessed via the path network from a fixed point of interest. PCAs
capture how well street coverage relates to a specific key destination. The basic calculation of a
PCA is to divide the area of a fixed distance by the area of the polygon that results by traveling
that distance from the key destination in question. The resulting polygon represents somehow
the walkable area compared with space around the destination. The data are presented as a
ratio between the Euclidean distance and the network distance (figure 14). Values of the ratio
close to one indicate extremely high walking conditions, while a score less than 0.30 would
reflect an inaccessible walking environment. Even if there has not been enough research to
determine an optimal PCA score, it is suggested that a minimum score of 0.5-0.6 is a useful
threshold (Schlossberg, 2006).
FIGURE 15: PEDESTRIAN CATHCMENT AREA (SCHLOSSBERG, 2006)
For the Nomentano district analysis, the PCA values will be provided for all the 231 transit
stops, in which they represent the central point. Later in the research, these values will be used
also for the evaluation of the accessibility of each stop; in this context, a mean value indicates
the general walkability of the neighborhood.
47 3. Thesis Purpose and Methodology
3.3.2 TRANSIT ACCESSIBILITY INDEX
This is the core point of the methodology, the most important according to the final aim of the
entire work. Seven indicators will be provided for the 231 stops, describing their accessibility
for pedestrian users. They are listed in the following table:
Accessibility Index
Number of Lines
Frequency
Land Use Entropy
Level of Service
Pedestrian Catchment Area
Inhabitants served
Level of Comfort
TABLE 6: ACCESSIBILITY INDEX
The choice of the indicators is not casual and it has been done through an in-depth analysis of
the literature treating arguments of urban design, walkability and accessibility. One of the aims
of the project is to provide an index that can be flexible and applicable in several situations and
environments. For instance, each indicator assumes different relevance depending on the
environment chosen, the frequency is really important for urban public transport, but it’s less
important for extra urban journeys where punctuality is preferred.
In addition, each indicator can acquire different importance with respect to the final goal of the
project. For example, if the study considers the disability people’s needs as the first objective to
achieve, the level of comfort and the level of service of the stop assume more value. If the study
is evaluating the location of a bus stop linking a residential area to a city center, the service
area’s inhabitants assume more value. Again, if the bus stop considered is serving an area
where particular events take place attracting a lot of people, the frequency indicator assume
particular value. Bus stops serving students and children may need higher pedestrian
catchment area, meaning higher walkability value. Bus station stops need higher number of
lines and so on.
More over the indicators have been chosen as to include qualitative and quantitative measures,
indeed the factors are somehow complementary. If the number of lines, the frequency or the
PCA provide objective information, the level of comfort comprehends qualitative and subjective
considerations, also collected by the survey.
The index comprehends several scales of evaluation: the PCA and the inhabitants served relate
to urban planning and road network, LOS and level of comfort describe the bus stop
48 3. Thesis Purpose and Methodology
infrastructure and its furniture, while number of lines, frequency and land use entropy
characterize the transit service.
In the next paragraph a brief description of each indicator is provided.
3.3.2.1 INTRODUCTION TO THE INDICATORS
In this paragraph the indicators composing the index are described briefly, as to understand
their characteristic and their meaning. The entire hypothesis, the assumptions and the
calculations used are presented and analyzed in depth in the next chapter, case by case.
The number of lines indicates the total lines serving a specific stop. Each stop has its own
specific number. Its meaning it’s straightforward to understand, the higher the number of lines,
the more attractive is the stop.
However the number of lines is not enough to evaluate the efficiency and the quality of the
service, the frequency is an important and attractive measure. It is calculated as the number of
vehicle serving the stop per hour. Again, the higher the value, the more attractive is the stop,
providing a better service, where 0 means no vehicle serving the stop and 1 means 60 vehicle
per hour. Since a single stop can be served by several lines, the frequency of the stop is
calculated as the average among the frequencies of the lines passing through that stop. In this
way, the feature of the stop can be somehow lowered, because the frequencies of some line may
be overestimated and other underestimated. However, since all the indicators are joined
together for the final evaluation of the pedestrian accessibility of the stop, the higher number of
lines may compensate this variance in the calculation of the frequency.
The land use entropy factor (Rian, Ewing, 2017) measures the land use diversity, it is the degree
to which different land uses within the buffer are balanced in floor area. If the number of
categories chosen is n, the formula to calculate the LUE within the service area is the following:
The value of LUE floats between 0 and 1, in which 1 means the perfect distribution of the
building’s typology and 0 means the total predominance of one typology. Of course, as
49 3. Thesis Purpose and Methodology
explained in the second chapter, land use mix is a fundamental attracting characteristic in urban
planning and evaluation, thus the higher the value of LUE within the area covered by the line,
the more attractive is the line itself. The land use entropy is calculated for each line passing
through Nomentano district, so, again, the value associated to each stop is calculated as the
average among the lines serving that specific stop. Also for this factor, the same assumptions
about variance from the average values can be made. The categories of building considered for
the calculation are the following: residential, industrial, commercial and public. Again, all the
considerations and the specific calculations and assumptions are treated in depth in the next
chapter.
Personal space is a requirement for both comfort and safety (“Designing for Sustainable
Transportation”, Winnipeg Transit). If people waiting at a bus stop are forced to stand to close
to one another, they may find it uncomfortable and avoid using the bus. Safety is also a concern,
because if there is too little room for the number of people waiting for a bus, some people may
be forced to stand too close to the curb. The jostle of passing pedestrians, as well passengers
getting on and off buses can also force people out into the road and into harm’s way. Therefore
adequate personal space is a requirement at all bus stops. As a general rule, Winnipeg Transit
recommends a Level of Service of no less than C, according to the table at left. This allows for
free or restricted circulation in most cases, while still maintaining a personal comfort zone
during peak hours and other times of unusually high transit use.
Level of Service Definition Diameter (m) Occupied Space (m²)
A Free Circulation >1.22 na
B Restricted Circulation 1.07 - 1.22 1.17
C Personal Comfort Zone 0.92 – 1.07 0.90
D No Touch Zone 0.61 – 0.92 0.66
E Touch Zone <0.61 0.20
F Body Ellipse - -
TABLE 7: LEVEL OF SERVICE DEFINITION
The Pedestrian Catchment Area indicator has been already explained in the previous
paragraph. However, considering the bus stop as the central point of the calculation, PCA is an
indicator of the accessibility of that specific stop according to the road network. In order to
respect the pedestrian behavior, theoretical walkable zones are mapped to show the actual area
and network within a 400 meters walk distance from the bus stop. Again, as the value of PCA
approaches to one, the easier is to reach the bus stop from the service area, meaning higher
50 3. Thesis Purpose and Methodology
accessibility. If the PCAs overlap, the area is well covered and served by transit, allowing
people to choose the stop according to other types of evaluations, comfort, frequency etc. If the
distribution of PCA points out some areas not served, it means that the transit service is not
well spread over the neighborhood. Moreover if the value of PCA is less than 0.30, the walking
environment is almost inaccessible and so also the bus stop is almost impossible to be reached.
The inhabitants served indicator is a feature which suggests the total number of population can
access the closer transit stops. Thus, it can be useful to estimate the correct stops’ location
according to the number of potential users served. The service area is then used to help
understanding the existing potential demand. As already explained, Euclidean buffers (circular
buffers around a point) overestimate the service area of a stop leading to several errors when
estimating the demand for transit. For this reason, network buffers are preferred, they are better
approximations of actual service areas structures and shapes. The size of the service area has
been chosen according to literature review, transit industry widely applies the 400 meters rules
of thumb when estimating service areas around bus stops. The service area is then calculated as
the area of the polygon representing the walkable space reachable walking 400 meters from the
bus stop. Since precise data are not available about the population for each building within the
service area, some assumptions have been used and they are treated deeply in the next chapter.
The last indicator used is the one describing the Level of Comfort of the stop. Even if it
generally involves subjective considerations, it has been necessary to include some objective
rules as to rank the comfort equipment of each stop. This indicator can seem similar to the one
describing the level of service, however while the latter is related only to the personal space
available in the waiting platform, the level of comfort ranks the quality of the stop according to
its equipment. Seven levels have been identified according to the characteristics of the stops in
the district.
3.3.3 IDEAL POINT METHOD
For a total comprehensive analysis a Multicriteria Decision Analysis is proposed. The final goal
is to obtain a value that englobes all the characteristics pointed out by the 7 indicators and
describes univocally the accessibility of each stop of Nomentano district. In particular the
method proposed focuses on multicriteria decision rules. A decision rule is a procedure that
allows for ordering alternatives. The decision rule dictates how best to order alternatives or to
51 3. Thesis Purpose and Methodology
decide which alternative is preferred to another. It integrates the data and information on
alternatives and decision maker’s preferences into an overall assessment of the alternative.
Specifically, ideal point method orders a set of alternatives on the basis of their separation from
the ideal point (Malczewski, 1999). This point represents a hypothetical alternative that consists
of the most desirable weighted standardized levels of each criterion across the alternatives
under consideration. The alternative that is closest to the ideal point is the best alternative. The
separation is measured in terms of a distance metric. Using a generalized family of distance
metrics, the ideal point decision rule is:
where is the separation of the ith alternative from the ideal point, is a weight assigned to
the jth criterion, is the standardized criterion value of the ith alternative, is the ideal
value for the jth criterion, and p is a power parameter. In general, larger values of p reflect
greater concern for minimizing the maximum separation from the ideal.
The ideal point can be considered as one of many possible points that can be used for ordering
the set of feasible alternatives. The negative ideal alternative can be constructed in a similar
way, it consists of the worst weighted standardized levels across the alternatives. The following
measure of separation from the negative ideal can be used:
where is the worst value of the jth criterion (the negative ideal). The best alternative is
characterized by the maximum separation from the negative ideal.
The procedure involves the following steps:
1. Determine the set of feasible alternatives.
2. Standardize each attribute by transforming the various attribute dimensions ( ) to
unidimensional attributes ( ; this transformation allows for comparison of the
various attributes.
3. Define the weighs assigned to each attribute; the set of weights must be such that
0 and = 1.
4. Construct the weighted standardized vectors by multiplying each value by the
corresponding weight .
5. Determine the maximum value ( ) for each of the weighted standardized vector, that
is = ( ).
52 3. Thesis Purpose and Methodology
6. Determine the minimum value ) for each of the weighted standardized vector, that
is = ( ).
7. Using a separation measure, calculate the distance between the ideal point and each
alternative; a separation can be calculated using the Euclidean distance metric:
8. Using the same separation measure, determine the distance between the negative ideal
point and each alternative:
9. Calculate the relative closeness to the ideal point using the equation:
where 0 < < 1; that is, an alternative is closer to the ideal point as approaches 1.
3.3.4 PAIRWISE COMPARISON METHOD
In order to weigh the attributes for the ideal point method, the pairwise comparison method is
applied. The PCM was developed by Saaty (1980) in the context of the analytic hierarchy
process (AHP). This method involves pairwise comparisons to create a ratio matrix. It takes as
an input the pairwise comparisons and produces the relative weights as output. Specifically, the
weights are determined by normalizing the eigenvector associated with the maximum
eigenvalue of the reciprocal ratio matrix.
The indicators considered for the study are strongly interconnected and often they influence
themselves. So the best way to weigh their importance is to compare them in pair, as to
appreciate, case by case, which one is considered more important. It is not sufficient to simply
rank them from the most important to the least important, it is fundamental to understand each
relation in order to weight them in a more precise way.
The procedure consists of three major steps: generation of the pairwise comparison matrix, the
criterion weights computation and the consistency ratio estimation.
The method employs an underlying scale with values from 1 to 5 to rate the relative preferences
for two criteria.
53 3. Thesis Purpose and Methodology
Intensity of importance Definition
1 Equal importance
2 Moderate importance
3 Strong importance
4 Very strong importance
5 Extreme importance
TABLE 8: SCALE FOR PAIRWISE COMPARISON
It has been made the assumption that the comparison matrix is reciprocal; that is, if criterion A
is twice as preferred to criterion B, criterion B is preferred only one-half as much as criterion A.
Thus, if criterion A receives a score 2 relative to criterion B, criterion B should receive a score of
when compared to criterion A. Clearly, comparing anything to itself, the evaluation scale must
be 1, representing equally preferred criteria.
The computation of the criterion weights involves the following operations: sum the values in
each column of the pairwise comparison matrix; divide each element in the matrix by its
column total; compute the average of the elements in each row of the normalized matrix, that is,
divide the sum of the normalized scores for each row by the number or criteria. These averages
provide an estimate of the relative weights of the criteria being compared.
The last step is the estimation of the consistency ratio, determining if the comparisons are
consistent. It involves the following operations: determine the weighted sum vector by
multiplying the weight for each criterion times the column of the original pairwise comparison
matrix for that criterion, and finally sum these values over the rows; determine the consistency
vector by dividing the weighted sum vector by the criterion weights determined previously.
Once calculated the consistency vector, lambda (λ) and the consistency index (CI) are
computed. The value for lambda is simply the average of the consistency vector.
The calculation of CI is based on the observation that λ is always greater than or equal to the
number of criteria under consideration (n) for positive, reciprocal matrixes, and λ = n if the
pairwise comparison matrix is a consistent matrix. Accordingly, λ – n can be considered as a
measure of the degree of inconsistency. This measure can be normalized as follows:
The CI term, referred to as the consistency index, provides a measure of departure from
consistency. Further, the consistency ratio (CR) can be calculated as follows:
54 3. Thesis Purpose and Methodology
Where RI is the random index, the consistency index of randomly generated pairwise
comparison matrix. RI depends on the number of elements being compared (table 9).
n RI n RI n RI
1 0.00 6 1.24 11 1.51
2 0.00 7 1.32 12 1.48
3 0.58 8 1.41 13 1.56
4 0.90 9 1.45 14 1.57
5 1.12 10 1.49 15 1.59
TABLE 9: RANDOM INCONSISTENCY RATIO (SAATY, 1980)
The consistency ratio (CR) is designed in such a way that if CR < 0.10, the ratio indicates a
reasonable level of consistency in the pairwise comparison; if CR 0.10, the values of the ratio
are indicative of inconsistent judgements. In such cases one should reconsider and revise the
original values in the pairwise comparison matrix.
3.3.5 QUESTIONNAIRE
In order to make the weighting criterion the most objective possible, a questionnaire has been
supplied to Master students, PhD students and professors experts in transportation field. As
explained before, the indicators chosen make the index flexible and modifiable according to
specific goals, projects or even personal view. Thus, the final goal of the survey is to collect the
preferences of the interviewed and feed the matrix of the pairwise comparison model, as to
obtain a proper weight of each indicator.
It has been asked to fill the questionnaire comparing each couple of indicators and expressing
one preference according to the scale of pairwise comparison of table 8. An example is shown in
figure 15. Since the indicators are 7 and the matrix is positive and reciprocal, there are 21
comparisons (with n criteria, it involves n(n-1)/2 comparisons).
FIGURE 16: EXAMPLE OF THE QUESTIONNARE
A deeper analysis of the results is treated in the fourth chapter.
55 4. Application: Case of Study
4. APPLICATION: CASE OF STUDY
In this chapter the methodology explained in the previous chapter is developed for the case of
study: Nomentano neighborhood. For each indicator the assumption and the procedure are
treated explaining the reasoning behind every choice that distinguishes the used method from
the rigorous one. Moreover, since most of the analysis has been done using the software
ArcGis10.2.1, also the fundamental steps and commands applied are mentioned. The first part of
the chapter deals with the general characteristics of the urban structure of the neighborhood,
analyzing the road classification, the number of intersection and dead-ends and the Pedestrian
Catchment Area. The central part treats specifically the seven indicators, calculated for each one
of the 231 bus and tram stops.
4.1 STREET NETWORK ANALYSIS
4.1.1 ROAD CLASSIFICATION
The streets classification analysis addresses the request to access road functionality, defining the
relationship of impedance roads, hostile to pedestrians, and accessible roads. Identifying the
roads with their functional classification can help to understand immediately the pedestrian
friendliness of the urban environment.
The classification of the streets of the district has been made according to “Regolamento viario
e classifica funzionale delle strade urbane di Roma Capitale”(2015). It provides the functional
characteristics of the roads and their classification, moreover a list of the all the set of Roman
roads is served.
The typologies of road involved in the analysis of the neighborhood are: scorrimento,
interquartiere, quartiere, interzonale, locale. The first one is the most hostile to pedestrian
environment since it constitutes a real barrier, on the contrary local roads are generally more
walkable because of decreased speeds and automobile volume.
The figure 16 highlights the different typologies with different colors, as to give an immediate
impact and to make easier the identification of the most walkable areas.
Starting from the red one, representing strada a scorrimento, it must be said that it mostly
develops underground, so it does not strongly affect the pedestrian environment. It is the so
called Tangenziale Est, one of the main urban arterial of the city, linking Porta Maggiore to the
northern area of the city of Rome.
Strade interquartiere are three: Via Nomentana, Via dei Monti Tiburtini and Viale Castro
Pretorio. However the only one which plays a central role for the neighborhood is via
56 4. Application: Case of Study
Nomentana. It is the most important road of the district and a lot of public and private activities
take place along it.
Other important roads that delineate the network of the district are viale Regina Margherita -
viale Regina Elena and viale del Policlinico - via G.B. Morgagni - viale della Lega Lombarda.
Those two roads and Circonvallazione Nomentana contribute to give a triangular structure to
the district, in which Piazza Bologna represents the barycenter and it is well connected to the
main arterial through a star shaped network. The area comprehended within this triangle seems
to have a high connectivity, since there are a lot of local roads creating a pedestrian friendly
environment.
FIGURE 17: ROADS CLASSIFICATION
4.1.2 INTERSECTION INTENSITY ANALYSIS
For the analysis of the intersections some assumptions have been made about the structure of
the pedestrian environment. The layer representing the road network, provided by
OpenStreetMap, comprehends every typology of link, not only the main ones accessible by car.
Then, considering all this layers in evaluating the number of intersections brings to an
overestimation of it, not reflecting the real pedestrian network and the connectivity of the
neighborhood.
57 4. Application: Case of Study
In particular, the following typologies are deleted from the analysis: bridleway, cycle way,
disused, footway, funicular, groyne, light rail, living street, monorail, motorway, narrow gauge,
path, pedestrian, pier, preserved, raceway, rail, service, steps, subway, track, tram, unclassified.
In such a way the network reproduces only the road skeleton composed by the roads classified
in the previous paragraph, assuming that sidewalks and streets are synonymous and off-streets
paths, represented by the typologies listed above, are rare due to the residential nature of the
neighborhood.
The considered area is again an extended area through the Buffer tool, with 400 meters distance,
that is the assumed distance a pedestrian is willing to walk to access the bus stop.
The first step is to identify the number of dead ends within the neighborhood. This step is
realized by the tool Feature Vertices to Point which creates a feature class containing points
generated from specified vertices or locations of the input features. Since dead-ends are sought,
dangle type should be put in the point type cell (figure 17).
FIGURE 18: DEAD ENDS TOOL
The total number of dead ends founded is 79 (figure 18). As it can be seen in the picture below,
the dead ends are more or less equally spread over the neighborhood. However there are some
areas where the density is a bit higher, for example in the north east and north west zone, or in
the central zone, just below Piazza Bologna. For sure this affects the connectivity of that specific
zone and, in particular, the PCA of the stops located in those areas may be lower. Every further
consideration and its relation with the accessibility will be treated in the PCA paragraph.
58 4. Application: Case of Study
FIGURE 19: DEAD ENDS DISTRIBUTION
About the intersections, they have been found creating the network dataset starting from the
feature class previously considered for the research of the dead ends, that is without those links
which can overestimate the connectivity and intersections actually walkable by pedestrians.
Some other assumptions have been made for a more precise analysis. Even if creating the
network dataset the software automatically finds all the intersections, it has to be specified that
it considers dead ends as normal junction and moreover it identifies all the intersections
between two lines as a junction, that is not precise and it does not reflect the reality structure of
the network. While the problem about reconsidering the dead ends can be solved manually, the
second involves higher numbers so it requires specific attention. In particular, it creates heavy
problems in the major roads, since they are represented in the feature class by more than one
line: Circonvallazione Nomentana, Via Nomentana, Viale Regina Margherita, Viale Regina
Elena, Via Giovanni Battista Morgagni, Via Bari, Via Catania, Via della Lega Lombarda. In
order to avoid this problem, the tool integrate has been used: it is used to maintain the integrity
of shared feature boundaries by making features coincident if they fall within the specified x,y
tolerance; features that fall within the specified x,y tolerance are considered identical or
coincident. In this case a tolerance of 20 meters has been fixed, however the major streets have
59 4. Application: Case of Study
been checked manually again as to avoid mistakes rising from particular cases. In the end, the
total number of intersections is 919. The figure 19 shows a good distribution of intersections,
suggesting a satisfying connectivity of the road network, particularly in Piazza Bologna area,
where the residential buildings are concentrated and thus, providing a good pedestrian access.
Anyway, there are some areas where it seems there are no intersections, in the south part of the
district and also along Via Nomentana. This is due to the presence of the cimitery and the
campus “La Sapienza” or public parks like Villa Torlonia or Villa Paganini.
FIGURE 20: INTERSECTIONS DISTRIBUTION
The characteristics of the neighborhood are sum up in the table 8. The most important value is
the ratio between intersections and dead ends, showing that the number of intersections is more
than ten times higher than the dead ends one, this suggests that the area may be highly
walkable with few potential barriers. On the contrary the densities of dead ends and
intersections seem low with respect to literature review, which identifies good walkable areas
with an intersection density varying from 100 to 150 mi². However as already explained, large
areas of the district provide public service where there is no road accessible.
60 4. Application: Case of Study
Dead-end Intersections Intersection:Dead-end
ratio
Area (m²) Dead-end
density (n/km²)
Intersection
density (n/km²)
79 919 11,63 8732729,15 9,05 105,24
TABLE 8: ROAD NETWORK CHARACTERISTICS
4.1.3 PEDESTRIAN CATCHMENT AREA AS INDICATOR OF URBAN
CONNECTIVITY
As explained in paragraph 3.3.1.3 PCA is a useful tool to show the actual area that can be
accessed via the path network from the transit stop walking for a distance of 400 meters. The
roads considered in the network are the same of the previous paragraph, however the area will
be a bit larger than the previous one, since the catchment area of the stops located close to the
boundary overpasses it.
The PCA value is used also for evaluating the accessibility of the single stop later on, however
in this paragraph it is important to evaluate the connectivity of the overall district. For this
reason, an average value of PCA is provided and moreover a picture showing the diversity of
PCA over the district suggests the most connected areas and the pedestrian hostile ones (figure
aaa).
The network defined pedestrian area has been calculated using a network analysis tool. First of
all, a network has been created using the assumption that sidewalks and streets are
synonymous. Then, the service area solver has been implemented. It generates polygons that
encompass all edges within a given distance, in this case the facilities are represented by the 231
stops and the maximum distance is 400 meters. About the layer properties is important to say
that u-turns at junctions are allowed and there is no restriction for one-way, since it is supposed
that walking path direction is only up on the pedestrian willingness itself (figure 20).
FIGURE 21: SERVICE AREA SOLVER PROCEDURE
61 4. Application: Case of Study
The average value of PCA is 0,4923. Values of the ratio close to one indicate extremely high
walking conditions, while a score less than 0.30 would reflect an inaccessible walking
environment. According to the actual research, it is suggested that a minimum score of 0.5-0.6 is
a useful threshold. The clearer areas of the picture 21 mean less connectivity. As can be seen
they are located in the lowest part, between the cemetery and the campus. This means that,
even if the area comprehends attractive public spaces, it does not have an high connectivity.
Furthermore the northern east part results again few connected, as already shown by the
previous indicator. The darker areas bringing higher connectivity develop along the main roads
and, in particular, along Via Giovanni Battista Morgagni – Via Bari – Via Catania – Via della
Lega Lombarda and Piazza Bologna; the latter seems already the central focal point of the
neighborhood.
FIGURE 22: PCA OF THE PUBLIC TRANPORT STOPS
4.2 ACCESSIBILITY INDEX
4.2.1 NUMBER OF LINES
The first indicator characterizing the stops is the number of lines they serve, the higher the
number, the more accessible is the stop.
62 4. Application: Case of Study
The information about each stop is available in the real time service provided by ATAC website,
it lists all the lines serving the stop chosen. However, the night lines and the dedicated lines
have not been considered, since they are not considered relevant for the final goal of the study.
The stops have a number of lines varying from 0 to 11, specifically the are distributed as follows
(table 9), with an average value of 3 lines per stops.
Number of Lines Number of Stops
1 73
2 47
3 53
4 32
5 9
6 8
7 5
8 3
11 1
TABLE 9: NUMBER OF LINES SERVING THE STOPS
The table shows that 90% of the stops are served by a number of lines between 1 and 4. The
others are mostly located in the southern part of the map, due to the presence of Termini
station, which is a focal central point for all the public transport in Rome, or along via Tiburtina,
one of the most important arterial of Rome.
FIGURE 23: PERCENTAGE OF NUMBER OF LINES PER STOP
The following figure presents all the stops giving a first visual impression of their serving lines’
number. As the legend explains, the radius of the circles is proportioned to the stops’
characteristic. It is interesting to point out that many times the higher density of bus stops
32%
20% 23%
14%
4% 4% 2% 1% 0%
1 2
3 4
5 6
7 8
11
63 4. Application: Case of Study
corresponds to a lower number of serving lines per stop, for example in Piazza Bologna area, in
Tiburtina station or in Piazzale del Verano.
FIGURE 24: NUMBER OF LINES PER STOP DISTRIBUTION
The list of the characteristic for each stop is provided in the appendix D.
4.2.2 FREQUENCY
Nomentano district is crossed by 45 different bus and tram lines, each one has its own
frequency (see appendix A). Again, the data available have been collected from the real time
service provided by ATAC website.
Here some basic information about line frequencies are presented. First of all, frequency is
expressed as number of busses passing through the stop per minute or per hour. The higher the
frequency, the higher is the accessibility of the stop. The highest frequency has been registered
for line 60, with a frequency of 0,1667 bus/min, that is 10 bus/h, serving via Nomentana; while
the lowest frequency is 0,0333 bus/min, that is 2 bus/h, for line 441 and 450. The average
frequency among the 45 lines considered is 0,0785 bus/min, corresponding to almost 5 bus/h.
However, the goal of the study is to evaluate the characteristic of each stop and not of the single
lines. So in order to calculate the frequency of the stop, the average among the frequency of the
64 4. Application: Case of Study
serving lines has been made. As already explained this methodology clearly overestimates the
frequency of some lines and underestimates the frequency of some other lines, but the final
evaluation integrates all the indicators and somehow it balances these assumptions for the
singular bus stop frequency. It has to be specified that the highest frequency has been reached
from a stop located in Stazione Tiburtina, which is served only from line 409 having a frequency
of 8 bus/h, thus it is not affected by any underestimation. The lowest frequency also has been
registered in a stop served only from one line, again located in Stazione Tiburtina, but in this
case it is not affected by any overestimation (figure 24).
FIGURE 25: FREQUENCY OF THE BUS STOP
The list of the frequency for each stop is provided in the D.
4.2.3 LAND USE ENTROPY
The land use entropy factor (Rian, Ewing, 2017) measures the land use diversity, it is the degree
to which different land uses within the buffer are balanced in floor area. The categories
considered in the study area are the following: residential, commercial, industrial and public.
While industrial and residential data have been collected from Openstreetmap, the information
about commercial and public ones have been provided by the site of Roma Capitale. In particular
140000 punctual data were provided for commercial activities, while public activities englobe
churches, cemeteries, hospitals, schools, sports buildings, amusement parks, public gardens and
archeological areas. The data provided by Roma Capitale are in CSV format, so it has been used a
Python module in order to link the addresses of the CSV file to APIs. In particular in this work
the API from Openstreetmap “Nominatim” has been used, fixing a time gap of 5 seconds
between two operations not to overload the servers. The results obtained thanks to this process
0,0
1,0
2,0
3,0
4,0
5,0
6,0
7,0
8,0
9,0
Fre
qu
ency
(b
us/
h)
Frequency of the bus stop
65 4. Application: Case of Study
have been the latitude and longitude of the commercial activities, that have been added to
Arcgis maps. The Python code is presented in appendix F.
For an easier analysis, also the data provided by Openstreetmap, originally polygons, have been
turned to punctual data through the tool Feature to point of Arcgis, which creates a feature class
containing points generated from the representative locations of input features (figure 25).
FIGURE 26: FEATURE TO POINT PROCESS
The formulas used for calculating the Land Use Entropy are the following:
However it is really important to specify which areas are taken into consideration, as to collect
correctly the number of building for each of the four typologies and the total number of
buildings.
The meaning of this indicator is to evaluate the attraction of each stop, that means evaluating
the attraction of the different lines serving that specific stop. So the methodology follows the
opposite direction: first of all, the LUE has been calculated for each line and then the average
has been estimated for each stop, according to the number of lines serving that specific stop.
The area covered from each line should be specified, not only within Nomentano district, but all
over the area of the city of Roma. Since as already deeply treated, a 400 meters distance has
been fixed as the path a pedestrian is willingness to walk to reach the bus stop, the area covered
by each line is the sum of all the pedestrian areas defined by the road network starting from the
bus stops composing the line itself.
In the picture below the service areas of the two tram lines are shown, as an example to clarify
the concept.
66 4. Application: Case of Study
FIGURE 27: SERVICE AREAS OF THE TWO TRAM LINES
The service areas have been calculated with the same process explained before for the
pedestrian catchment area, that is with service area solver.
Once found the service areas for each line, it is possible to calculate the LUE. Residential,
commercial, industrial and public buildings are now punctual data, so it is sufficient to select all
the buildings within the service area and distinguish them according to their typology. Then it
is straightforward to compute the value of LUE for each line.
An example of the process is provided here, while the total results are presented again in the
appendix C.
FIGURE 28: BUILDINGS TYPOLOGY DISTRIBUITION OVER LINE 309 SERVICE AREA
67 4. Application: Case of Study
As can be seen from the picture, the majority of the buildings is represented by residential and
commercial ones, respectively green and blue points. Industrial (red) and public (yellow)
buildings are usually concentrated in specific areas.
Line 309 Residential Public Commercial Industrial Total LUE
Number 3107 48 3429 97 6681 0,574
Proportion 0,465 0,007 0,513 0,015
LN -0,766 -4,936 -0,667 -4,232
TABLE 10: LAND USE ENTROPY CALCULATION FOR LINE 309
Then, in order to calculate the attraction of each stop, it has been computed the average value
among the LUE of the lines serving that specific stop. The values float between a maximum
value of 0,663, with the best attractiveness, of and a minimum value of 0,568, worst
attractiveness. The average value is 0,5675, represented by the red line in figure 28. So the
interval comprehending the values is quite small considering that LUE value goes from 0 to 1.
This is due to the fact that the large majority of buildings are composed by residential or
commercial activities, so the LUE is naturally lowered and homogenized.
FIGURE 29: LUE AVERAGE PER STOP
4.2.4 LEVEL OF SERVICE
The Level of Service is estimated according to personal space available in the stop area. The
fundamental characteristics needed for the calculations are the total available area ( ) and
the total occupied area ( ).
0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
0,9
1
Lan
d U
se E
ntr
op
y
LUE Average per stop
68 4. Application: Case of Study
The total available area is considered as the total space available for waiting for the bus
provided to the users. Since in many cases the lengths of these areas are not clearly defined,
some assumptions have been made: the areas have a total length from a minimum of 15 to a
maximum of 40 meters, according to the number of lines, and then of buses, serving the stops.
For a more precise analysis, all the stops have been inspected using Google Maps, as to define the
width of the walk side or of the pedestrian island and to point out the specific characteristics of
the stops. Moreover two buffers of 0,2 meters along all the length of the stop area have been
subtracted.
The total occupied area is considered as the total space occupied by users waiting for the bus. It
is done by the product between personal space occupied and number of users. The procedure
starts with the maximum theoretical personal space occupied (level A), then if it does not satisfy
the condition >
, the same procedure is repeated with personal space of level B and so
on. Some assumptions have been made in order to estimate the number of users, since there are
no data available about number of users per stop provided by the municipality of Rome. In
particular, six bus stops have been detected for 20 minutes each one, as to collect data about
people boarding, alighting or simply waiting the bus. The stops chosen have different number
of lines and one of them is served only by tram, in order to have various types of data
collection.
Once calculated these stops characteristics, the fraction R= /
is calculated. If the result
is lower than 1, it means that the level of service chosen is correct; if the result is equal or higher
than 1, it means that a lower level of service is needed.
FIGURE 30: PERCENTAGE OF LOS DISTRIBUTION
78%
12%
6% 4%
Percentage of LOS distribution
A
B
C
D
69 4. Application: Case of Study
As said before, general rules about comfort and safety recommend LOS of no less than C. In the
study, only the 4% of the stops are characterized by a LOS lower than C, since they are actually
extreme cases.
4.2.5 PEDESTRIAN CATCHMENT AREA AS INDICATOR OF BUS STOP
ACCESSIBILITY
The Pedestrian Catchment Area can be used as an indicator describing one of the aspects
characterizing the accessibility of a stop. In particular it indicates the connectivity of that stop
and the easiness to be reached by pedestrians. As the methodology to calculate it has been
already explained, in this paragraph the attention is focused on the resulting PCA for each stop.
The following picture represents each stop with a color according to its PCA, the values have
been categorized in only 5 classes in order to have an immediate idea of the connectivity of the
area. The stops with higher values of PCA are concentrated around Piazza Bologna and along
the main arterials of the district. On the contrary the lowest connected areas seem the one
between the campus and cemetery, and the north eastern one. If the first one can be justified by
the presence of two big public buildings (the internal service roads have not been considered in
the network), the second case is really critical.
FIGURE 31: PEDESTRIAN CATCHMENT AREAS
70 4. Application: Case of Study
Here is presented the example of the highest PCA value, reached by the stop “Regina
Margherita/Galeno”. The first figure represents the theoretical pedestrian service area, the
second one shows the network defined pedestrian service area and the last figure highlights the
pedestrian catchment area ratio.
FIGURE 32: COMPARISON BETWEEN HIGH AND LOW PCA
4.2.6 INHABITANTS SERVED
This indicator collects the number of potential users within the network defined pedestrian
service area. Since there is no available data about the population living in each building, some
assumptions have been made starting from physical characteristics of residential structures.
In particular, the number of floors per building has been estimated deviding the total height per
3, that is the assumed height of each floor; then one more floor has been removed, since usually
the first or the last floor of a residential building is not use for this purpose.
The next step has been to calculate the number of flat per floor: assuming that an average of 30
squared meters are used for the stairwell, this value has been subtracted from the total area,
which is then divided by 85 squared meters, the average flat area in the city of Rome.
In order to calculate the total number of flats per building, the number of floors has been
multiplied by the number of flats per floor.
71 4. Application: Case of Study
From the Census of the city of Rome of 2016, it has been calculated that the average number of
members per family in the second municipality is 1.9 people. This is due to the fact that the
majority (more than 50%) of households are composed by just one person (see figure 32).
FIGURE 33: DISTRIBUTION OF HOUSEHOLD'S MEMBERS
With these assumptions, it is possible to estimate the number of residents per building, simply
multiplying the total number of flats per building by the average number of household’s
members.
The total number of inhabitants living in the area considered has been calculated as the product
between the density inhabitants of the second municipality (Census of 2016) and the area itself.
The latter is the sum of all the network defined service areas of the 231 transit stops. Since this
value results lower than the corresponding one found with the mentioned assumptions, the
ratio between them has been applied to the number of residents per buildings, as to have a
more precise estimation of the data.
So, in the end, the number of inhabitants per building depends on its physical characteristics
and, even if it neglects some considerations, it may be considered as a consistent indicator of the
feature, assuming the starting data available.
The inhabitants served per each stop are estimated summing the residents of the buildings
within the network defined service area. The latter is again defined using the service area solver
provided by Arcgis.
The following picture shows the inhabitants distribution over the district. The most crowded
area seems the south west of the district, the population is quite equally distributed around the
central point constituted by Piazza Bologna, while the north east appears again the less
attractive.
52%
22%
14%
9% 2% 1% 0% 0%
Number of family member in
Municipality II
1 2
3 4
5 6
7 >7
72 4. Application: Case of Study
FIGURE 34: INHABITANTS DISTRIBUTION IN NOMENTANO DISTRICT
The results for each stop are provided in the appendix D. This time the interval is quite large,
since the number of inhabitants varies from 14 to 5840 with an average of 2571; two graphical
examples of extreme situations are presented below.
FIGURE 35: EXAMPLES OF INHABITANTS SERVED PER STOP
73 4. Application: Case of Study
4.2.7 LEVEL OF COMFORT
The level of comfort expresses the equipment and the position of the bus stop, ranking them
from 1 to 7. The goal of this indicator is to express through objective considerations the
subjective concept of comfort and safety. Here is the list of the levels.
Level of Comfort Description
1 Walk side + bus marker
2 Pedestrian island + bus marker
3 Walk side + bus marker + info panel
4 Walk side + shelter
5 Pedestrian island + shelter
6 Walk side + shelter + info panel
7 Pedestrian island + shelter + info panel
TABLE 11: COMFORT INDEX
For the classification, it has been considered that a generic user prefers pedestrian island to
walk side and stop with shelter to bus marker, this for simply safety and comfort reasons. More
over additional equipment is constituted by the electronical panel providing information about
the waiting time, which increases for sure the attractiveness of the stop itself.
The characteristic of each stop has been detected through an investigation using GoogleMaps.
Pedestrian islands are located mostly in the main arterials of the district, especially in the ones
tram provided, or in specific squares whose structures allow the placement of this kind of
equipment.
FIGURE 36: LEVEL OF COMFORT DISTRIBUTION
Again, the complete list of the all stops is provided in the appendix D.
51%
16%
2%
13%
17% 1% 0%
Level of Comfort distribution
1
2
3
4
5
6
7
74 5. Analysis of the Results
5. ANALYSIS OF THE RESULTS
This chapter presents the results of the different methodology’s steps, dividing each calculation
in paragraphs in order to clarify the analysis of their peculiarities and characteristics that
contribute to the final evaluation of the accessibility of the stops. The first paragraph shows the
responses of the questionnaire, which feed the Pairwise Comparison Model, whose results are
calculated in the second paragraph; the multicriteria analysis is applied in the third paragraph
and its outcomes are examined in the last one.
5.1 EVIDENCES FROM THE QUESTIONNAIRE
The goal of the questionnaire is to give a more objective perspective to the all work. As
explained before, the index contains different typologies of indicators which consider the urban
context, the qualities and facilities of the stops and the efficiency of the bus service. Due to this
feature, it is flexible and it can be modified according to the specific aim of the project
considered, for example giving more importance to the level of service or to the performance of
transit. However the scope of this work is to estimate the overall accessibility of the stops
without attributing specific weigh to some indicators according to the preferences of a singular
person. In fact, the author can be more sensible to some aspects, due to his personal experience
and perceptions, but they may not reflect the actual preferences and needs of the population
served. Then, through the results of a survey, an objective generic dimension is given to the all
work.
The questionnaire has been submitted to 41 people, Master students, PhD students and
professors experts in transportation field. It has been asked to fill the questionnaire comparing
each couple of indicators and expressing one preference. Since the indicators are 7 and the
matrix is positive and reciprocal, there are 21 comparisons (with n criteria, it involves n(n-1)/2
comparisons).
The results confirm the initial idea: it is difficult to find a common tendency preferring one
option to another or ranking equally the indicators. This supports the flexibility and
adaptability of the index, that should be calibrated according to the specific objective of the
project or to the decision makers will.
The two extreme cases are presented in the figures below. The comparison between frequency
and level of comfort is the most unbalanced, frequency is preferred almost three times to
comfort, meaning a strong preference.
75 5. Analysis of the Results
FIGURE 37: FREQUENCY - COMFORT COMPARISON
On the contrary, the most equilibrated case is the one comparing the land use entropy to the
level of service. As cleared by figure 37, the preferences are almost perfectly distributed over the
two options, without distinguishing a definite propensity.
FIGURE 38: LAND USE ENTROPY - LEVEL OF SERVICE COMPARISON
The final matrix with all the 21 comparisons has been built. It should be specified that for a
more detailed analysis, the comparison scale keeps the values with decimal factors, otherwise
the values would be more or less all equals, due to the high variety of preferences.
CRITERION N_lines Frequency LUE LOS PCA Inhabit_served Comfort
N_lines 1,00 0,45 1,63 1,27 0,91 0,68 1,78
Frequency 2,22 1,00 2,15 2,44 1,85 1,44 2,71
LUE 0,61 0,47 1,00 1,05 0,84 0,72 1,39
LOS 0,79 0,41 0,95 1,00 1,22 0,87 1,37
PCA 1,10 0,54 1,20 0,82 1,00 0,82 1,59
Inhabit_served 1,46 0,69 1,39 1,15 1,22 1,00 1,49
Comfort 0,56 0,37 0,72 0,73 0,63 0,67 1,00
TABLE 12: COMPARISON MATRIX
76 5. Analysis of the Results
From a quick view of the table, it is clear that the only indicator that exceeds several times the
value of two, standing for strong preference, is the one referring to the frequency of the lines. So
the large majority of the interviewed considers frequency as the most important indicator to
evaluate the attraction of the bus stop, attributing high value to the efficiency of the transit
service.
On the contrary, comfort is never preferred to any other indicator. This means that generally,
transportation experts do not consider the level of comfort as a priority for the users in
evaluating the accessibility of the stop. However, in some cases this value can perform an
important role. As always, the project should be contextualized and it is not easy to create an all
comprehensive cases index. A simple example: the largest percentage of the interviewees is
from Spain and Italy, typically countries characterized by Mediterranean climate; if the
questionnaire would have been submitted to people living in northern Europe, where the
climate is more hostile and rainy, comfort value may assume higher value. Again, surely the
importance of comfort would increase if the project is related to increase pedestrian accessibility
to people with physical disabilities.
5.2 CRITERION WEIGHING
The Pairwise Comparison Method has been already deeply explained in the third chapter. Here
results are presented and some comments are added. Here is the table of calculations for
determining the relative criterion weights:
CRITERION N_lines Frequency LUE LOS PCA Inhab_served Comfort
N_lines 0,13 0,11 0,18 0,15 0,12 0,11 0,16
Frequency 0,29 0,25 0,24 0,29 0,24 0,23 0,24
LUE 0,08 0,12 0,11 0,12 0,11 0,12 0,12
LOS 0,10 0,10 0,11 0,12 0,16 0,14 0,12
PCA 0,14 0,14 0,13 0,10 0,13 0,13 0,14
Inhab_served 0,19 0,18 0,15 0,14 0,16 0,16 0,13
Comfort 0,07 0,09 0,08 0,09 0,08 0,11 0,09
TABLE 13: DETERMINING THE RELATIVE CRITERION WEIGHTS
The relative weights of the criteria being compared is provided computing the average of the
elements in each row of the normalized matrix (table 13).
The list of the weights is presented below, ranking them from the most important to the less
important.
77 5. Analysis of the Results
Indicator Weight
Frequency 0,25
Inhabit_served 0,16
N_lines 0,14
PCA 0,13
LOS 0,12
LUE 0,11
Comfort 0,09
TABLE 14: RANKING OF THE INDICATORS ACCORDING TO THEIR WEIGHT
As expected, the most preferred indicator is the frequency and its weight is considerably higher
than the others. In fact, the other weights are more or less similar, due to the great variety of
preferences expressed by the interviewees, ranging from 0,09 and 0,16. However some more
comments can be made. The two indicators strictly describing the transit service are both high
ranked, that is, frequency and number of lines, suggesting the importance of the transit
efficiency. Then the three indicators describing the urban structure and the road network are
more or less all located in the central part of the ranking. In the end, the indicators describing
the quality of the stops are ranked at the lower part of the ranking. However, these two
particular indicators can play a fundamental role in specific projects when equity concept is
significantly important.
The consistency of the results has been checked through some calculations, expressed in the
following table. As can be seen, the consistency ratio is considerably lower than 0,10, indicating
a reasonable level of consistency in the pairwise comparison.
Weighted sum Consistency
Vector
N_lines 0,9701 7,0678
Frequency 1,7996 7,0778
LUE 0,7853 7,0471
LOS 0,8561 7,0491
PCA 0,9189 7,0620
Inhabit_served 1,1185 7,0739
Comfort 0,6158 7,0482
These weights will feed the multicriteria analysis, which results are presented in the next
paragraph.
λ Consistency Index Consistency
Ratio < 0,10
7,0608 0,0101 0,0077
TABLE 15: DETERMING THE CONSISTENCY RATIO
78 5. Analysis of the Results
5.3 MULTICRITERIA ANALYSIS
The procedure for the Ideal Point Method has been already described in chapter 3, then in this
paragraph all the assumptions are explained and the main results are shown. The detailed list of
the calculation is provided in appendix E.
First of all in the calculation of the separation measure, the power parameter p used is 2,
obtaining a straight-line distance.
However the strongest hypothesis is the selection of the ideal point, that is the most desirable
weighted standardized levels of each criterion across the alternatives under consideration. So
for the study the ideal vector is constituted by the best alternative for each indicator among the
231 stops considered. It is a sort of ideal stop that has all the best characteristics of the
Nomentano district’s stops. The choice of considering the best values among these stops is due
to the fact that the ideal stop will reflect the network and urban feature within the same
neighborhood, constituting a reliable comparison point.
Obviously, the negative ideal alternative has been constructed in the same way, considering the
worst alternative for each indicator.
In the following table the two extreme indicators are listed.
N_lines Frequency LUE LOS PCA Inhab_served Comfort
Maximum 1 0,03 0,52 2 0,23 14 1
Minimum 11 0,13 0,66 5 0,68 5840 7 TABLE 16: POSITIVE AND NEGATIVE IDEAL POINT VALUES
As can be easily understand, each one of the values has its own attribute and scale, it is then
fundamental to transform the values to an unidimensional scale. The results for all the
indicators of the stops are given as parts per unit with regard to the maximum for the area. The
values will have a score between 0 and 1 according to the following equation.
Where x represents the value of indicator i in its units.
Considering these assumptions, the values of the positive ideal point are always 1, while the
values of the negative ideal point are 0.
The weights assigned to each indicator are the ones of table 14, resulting from the questionnaire
and the application of the Pairwise Comparison Method.
The procedure explained in chapter 3 calculates the relative closeness ( to the ideal point,
that is, an alternative is closer to the ideal point as the value approaches 1. In this way, with a
single value it is possible to evaluate the accessibility of the bus stops according to the seven
indicators.
79 5. Analysis of the Results
A deeper analysis of the results is provided in the next paragraph, here generic results are
described with specific attention to the two extreme cases of the most and less accessible stops.
The highest value is obtained by the stop number 71359, that is V.le Regina Margherita/Nizza,
while the lowest is reached by the stop number 74169, Curioni/Repossi. The weighted
standardized values are presented in the following table, combined with a graph that points out
the relation and the distance from the positive ideal stop.
Stop_74169 Stop_71359 Ideal Stop
N_lines 0,0000 0,0137 0,1373
Frequency 0,0848 0,1907 0,2543
LUE 0,0184 0,0564 0,1114
LOS 0,0000 0,1214 0,1214
PCA 0,0373 0,1029 0,1301
Inhab_served 0,0204 0,1407 0,1581
Comfort 0,0000 0,0146 0,0874
TABLE 17: WEIGHTED STANDARDIZED VALUES FOR THE BEST AND THE WORST STOP
FIGURE 39: GRAPHIC COMPARISON WITH THE POSITIVE IDEAL STOP
The most attractive stop has high values of frequency, level of service, pedestrian catchment
area and inhabitants served. It means that it is well served by the transit system, it provides
good security solutions and that it is surrounded by a well-connected resident area. It is actually
located in one of the main arterial of the district, well linked to feeder local roads; the PCA
value is 0,5859, reflecting a pedestrian friendly road network (figure 39). Moreover the
surrounding area, in the southern-west part of the district, is one of the most populated, as can
be seen also in detail in figure 40.
0,00 0,05 0,10 0,15 0,20 0,25 0,30
N_lines
Frequency
Land-use
entropy
LOS PCA
Inhabitants
Comfort
Stop_74169
Stop_71359
Ideal Stop
80 5. Analysis of the Results
FIGURE 40: PCA OF V.LE REGINA MARGHERITA/NIZZA STOP
FIGURE 41: INHABITANTS SERVED BY V.LE REGINA MARGHERITA/NIZZA STOP
It is served by the two tram lines number 3 and 19, which have higher frequency than normal
bus lines. Some considerations should be made about the comfort and number of lines
indicators; the first one is really low with respect to the ideal one, but it has not an important
weight in the overall evaluation as the results of the questionnaire have highlighted. About the
number of lines, two tram lines serve the stop, that is a low number considering that the ideal
case comprehends 11. However, as figure 22 shows, the percentage of stop served by a number
of lines lower or equal than 2 is higher than 50%, so, again, in the overall ranking this indicator
does not play a crucial role.
Curioni/Repossi stop is the less attractive within the considered area. It is located in the
northern east part of the district, that presents a low density population and a poor
connectivity, as stressed already in the previous chapters and confirmed by the final results. In
particular, the following two figures show graphically the indicators PCA and inhabitants
served. The PCA value is 0,3614, that is really close to 0,3, considered as the minimum threshold
for a walking environment (Schlossberg, 2006). The theoretical number of served people,
81 5. Analysis of the Results
according to the made assumptions, is 765, considerably lower than the average of the
neighborhood, that is 2571.
FIGURE 42: PCA OF CURIONI/REPOSSI STOP
FIGURE 43: INHABITANTS SERVED BY CURIONI/REPOSSI STOP
All the three levels of analysis of the stop, urban network, transit service and stop furniture,
confirm the low accessibility of the stop. As already said it is located in a low connected and
poor populated area, more over it is reached by only one bus line, which quite low frequency is
0,0667 (4 bus/h). In the end also the quality of the stop is in a bad state, the LOS and the comfort
are at the lowest level, as can be seen in the picture below: there is no waiting space and the bus
stop dedicated area is occupied by parked vehicles.
82 5. Analysis of the Results
FIGURE 44: EXAMPLE OF LOS OF D (GOOGLE MAPS)
5.4 ACCESSIBILITY EVALUATION
In this paragraph the final analysis of the results is made. The most accessible areas are
compared with worst ones according to the results of the multicriteria analysis. Moreover some
specific cases are presented, as example of different situations and results.
The results of the multicriteria analysis are graphically presented in the picture below.
83 5. Analysis of the Results
FIGURE 45: BUS STOP FINAL ACCESSIBILITY
The red dots, representing higher accessibility values, are located mainly along the pricipal
arterials of the district: viale Regina Margherita, viale Regina Elena, via Giovan Battista
Morgagni, via Catania and via Nomentana. This confirms the importance of the road network
and urban design in evaluating the accessibility and the pedestrian environment. The main
arterials are always well connected to feeder roads, providing good pedestrian routes and
different path choices. The indicator that better explains this characteristic is the pedestrian
catchment area, infact it is higher in the areas close to the intersections between these streets
and in particular between viale Regina Margherita-viale Regina Elena, via Nomentana and via
Giovan Battista Morgagni. So, these arterials play a fundamental role in defining the
accessibility and the connecivity of this stops.
More over they constitute attractive points for the users, since lots of activities are placed along
them, both commercial and public. For this reason the number of serving lines and their
frequencies are higher than the majority of the neighborhood, reflecting again high accessibility.
Some other comments have to be made about the level of comfort and the safety of the stops
located in this main roads. Since they are larger than residential ones, often constituted by more
84 5. Analysis of the Results
lanes or even served by tram service, the stop area requests a protected zone where users can
wait in safety and comfort conditions for the service. Then, many times these stops are provided
by shelter and pedestrian islands, increasing the accessiblity of the area. In the figure 45 the best
stop is shown from the point of view of safety and level of comfort, situated along via
Nomentana.
FIGURE 46: NOMENTANA / XXI APRILE STOP
A particular case is Piazza Bologna, it represents the center of gravity of the triangular district.
The star shaped network helps to create an high interconnectivity with the sorrounded area, the
result of this urban structure is an high connected and well served central square, which
constitutes the central point of all the neighborhood.
On the contrary, the less connected area is located in the nothern part of the considered zone,
especially in the northern east, out of the border constituted by tha railway. Even if this area is
not administratively part of the district, it is interesting to highlight how this less connected and
poor populated zone corresponds to the lower values of accessibility for the bus stops (blue
dots in figure 45). As said in the previous chapters, all the indicators have low values in this
area, due to the characteristics of the neighborhood. Some extreme cases are presented now, in
particular the situations where the relative closeness to the ideal point is higher than 0,6 and
lower than 0,3. In this way, providing also graphical help, it is possible to understand and focus
on which indicators are more relevant to the final assessment and which ones vary the most.
The examples are also compared to the ideal situation.
5.4.1 BEST RESULTS
Five stops represent the best cases: Stazione Tiburtina (76895), Provincie/Padova (73354),
Bologna MB (73376), V.le Regina Margherita/Nizza (71359), V.le Regina Margherita/Nizza
(71267).
85 5. Analysis of the Results
Stazione Tiburtina stop is a bit singular case as it presents characteristics a bit different from the
others. It is served by only 1 line, but its frequency is extremely high, coincident with the ideal
stop of the neighborhood and, since frequency has the highest weight, this feature has a
significant impact on the overall evaluation. Moreover the level of service and comfort of the
stops are both notable, due to the fact that they are located in an intermodal station, served by
metro, rail and bus services. Also the value of the land use entropy is higher with respect to the
majority of the stops considered.
Provincie/Padova and Bologna MB stops are both served by bus lines 310 and 542, which
frequency is quite high: 6 bus per hour. These two stops presents suitable values for all the
indicators, from high efficiency service to a good urban connectivity. In particular, some
consideration for Bologna MB stop are made. As said before, it is located in the central point of
the district, then it is well connected due to the structure of the star shaped road network (high
PCA). Moreover the zone is highly residential, so even if the land use entropy value is not that
significant, the inhabitants served are many and that indicator has higher weight than LUE,
remarking its stronger impact on the overall evaluation.
The other two stops are located one in front of the other, that is the reason why most of the
indicators are equal: they are served by the same lines with the same frequency and they are
provided by the same furniture giving an high level of safety and comfort. The values of PCA
and inhabitants served are obviously slightly different because, even if the stops are nearby,
they are located in two different points and they are reachable through different paths. Since the
PCA is an input value for the calculation of the number of inhabitants served, also the latter is
different.
Stop_76895 Stop_73354 Stop_73376 Stop_71359 Stop_71267 Ideal Stop
N_lines 0,0000 0,0137 0,0137 0,0137 0,0137 0,1373
Frequency 0,2543 0,1695 0,1695 0,1907 0,1907 0,2543
LUE 0,0831 0,0538 0,0538 0,0564 0,0564 0,1114
LOS 0,1214 0,1214 0,1214 0,1214 0,1214 0,1214
PCA 0,0946 0,0996 0,1180 0,1029 0,0997 0,1301
Inhab_ served 0,0103 0,1263 0,1416 0,1407 0,1278 0,1581
Comfort 0,0582 0,0437 0,0437 0,0146 0,0146 0,0874
TABLE 18: BEST RESULTS INDICATORS VALUES
86 5. Analysis of the Results
FIGURE 47: BEST RESULTS GRAPHICAL REPRESENTATION
5.4.2 WORST RESULTS
Four stops represent the worst cases: Curioni/Repossi (74169), Pietralata/Monti Pietralata
(72507), Pietralata/Monti Pietralata (72488), Bencinvenga/Val Brembana (72490).
All the stops are located in the north-eastern part of the considered area, that it the less
connected and populated.
Two lines are serving the zone, with the same frequency of 4 buses per hour. Each analyzed
stop is reached by just one line, however the frequency is still acceptable and it does not
represent the main shortcoming. The comfort and the level of service of the stops are minimum,
without any waiting space or any facility provided, as shown in picture 43.
The values of land use entropy indicator seem satisfying with respect to the ideal value, anyway
this is due to the fact that the areas served by the bus lines are not densely populated, so the
higher value is caused by lower number of residential buildings rather than higher number of
other typologies’ buildings. In fact the inhabitants served indicator drop close to the negative
ideal value.
Another weakness of these stops is their less connectivity, indicated by the value of pedestrian
catchment area. As already said several time, the area is out of the administrative borders of the
neighborhood and it actually represented a poor populated and served area.
0,0000
0,0500
0,1000
0,1500
0,2000
0,2500
0,3000
Stop_76895
Stop_73354
Stop_73376
Stop_71359
Stop_71267
Ideal Stop
87 5. Analysis of the Results
Stop_74169 Stop_72507 Stop_72488 Stop_72490 Ideal Stop
N_lines 0,0000 0,0000 0,0000 0,0000 0,1373
Frequency 0,0848 0,0848 0,0848 0,0848 0,2543
LUE 0,0184 0,0591 0,0591 0,0591 0,1114
LOS 0,0000 0,0000 0,0000 0,0000 0,1214
PCA 0,0373 0,0401 0,0332 0,0099 0,1301
Inhab_served 0,0204 0,0048 0,0046 0,0176 0,1581
Comfort 0,0000 0,0000 0,0000 0,0000 0,0874
TABLE 19: WORST RESULTS INDICATORS VALUES
FIGURE 48: WORST RESULTS GRAPHICAL REPRESENTATION
5.5 ALTERNATIVE INDICATORS
A way to evaluate the fragmentation of the territory is studying its connectivity. This procedure
is not really related to the rest of the work, but it is a good additional parameter to estimate
practically the connectivity of the neighborhood. In particular, the selected function calculates
the least accumulative cost distance for each origin to a set of destinations. The calculation is a
function of the effective distance, which is the minimum distance between two points,
represented by the portals of each building and the bus stop (Ortega, Martìn, 2015).
Again, sidewalks and streets are synonymous and the street network is considered coincident
with the central axle of the roads. Each arc must include information on its length, travel speed
and travel time. In normal arcs the travel speed is calculated as 4 km/h, i.e. 1,1 m/s, and the
corresponding travel time is also calculated. Walking speed has become the subject of research
0,0000
0,0500
0,1000
0,1500
0,2000
0,2500
0,3000
Stop_74169
Stop_72507
Stop_72488
Stop_72490
Ideal Stop
88 5. Analysis of the Results
in the literature. This speed is consistent with the walking speed values found in the literature
(Gates, 2006).
5.5.1 COST DISTANCE FUNCTION
In order to develop a cost distance function a territorial matrix is proposed, representing the
cost for the displacement of the individuals. The cost is measured in time units and each arc has
a cost proportional to its lenght. In particular, since the time value for walking in an urban
environment is 6 €/h, according to the data provided by Civitas initiative, the cost of each arc is
given by the product of the travel time and the time value itself. In figure 48 a graphical
example of the resistance matrix of the roads sorrounding Piazza Bologna is provided.
FIGURE 49: RESISTANCE MATRIX
An additional cost can be added to the cost distance function, represented by the waiting time
at the bus stop. Each stop is characterized by an average frequency, so it is possible to calculate
the headway, that is the time between two consecutive bus arrivals. Then the average waiting
time for that stop is the half of the headway, since a generic user can arrive immediately before
the bus arrival, waiting 0 s, or immediately after the bus departure, waiting the all headway. So
the additional waiting cost is given by the product of the average waiting time and the value of
waiting time, provided again by Civitas initiative and corresponding to 2 € per hour.
Origins and destinations must be defined in order to evaluate the costs. As easily
understandable, the destinations set are the bus stops. The origins considered are all the portals
within the service area of the bus stop itself. So, in the end, the number of routes for each stop is
equal to the number of portals comprehended in its service area. For the calculation GIS
89 5. Analysis of the Results
function used is Closest Facilities from the tool network analysis of ArcGis software. The facilities
are the bus stops, while the incidents are the portals of the considered service area. Some
examples of costs are presented in the tables below.
Stop_Num Min
Cost Max Cost
Avg Travel
Cost
Frequency
[bus/min]
Avg Wait_time
[min] Wait_cost Tot_cost
stop_71267 0,0446 1,1292 0,5005 0,1083 4,6168 0,1539 0,6544
stop_71359 0,0280 1,3703 0,4732 0,1083 4,6168 0,1539 0,6271
stop_73354 0,0175 0,9151 0,4354 0,1000 5,0000 0,1667 0,6021
stop_73376 0,0350 0,8733 0,4352 0,1000 5,0000 0,1667 0,6019
stop_76895 0,1243 1,2756 0,4153 0,1300 3,8462 0,1282 0,5435
stop_72488 0,0211 1,0414 0,4973 0,0667 7,4963 0,2499 0,7472
stop_72490 0,0166 0,6671 0,3758 0,0667 7,4963 0,2499 0,6256
stop_72507 0,0061 0,9806 0,4691 0,0667 7,4963 0,2499 0,7190
stop_74169 0,0004 0,9525 0,3630 0,0667 7,4963 0,2499 0,6128
TABLE 20: EXAMPLES OF COST DISTANCE FUNCTION
5.5.2 POTENTIAL ACCESSIBILITY INDICATOR
In order to weigh the travel time in an appropiate way according to the population involved, a
new indicator is introduced, the potential accessibility indicator (López-Suárez, 2014):
where Cj is the weighted cost to reach the stop j , Tij is the impedance: travel time by the
minimal route through the network between portal i and the stop j , and Pi is the population
within the service area of the stop j. The population involved in each route is used as a weight
in order to value the importance of the minimal-time routes.
The indicator has been applied to a set of chosen stops, in particular the five most accessible and
the four less accessible stops according to the analysis explained in the previous chapters. The
number of inhabitants per building has been calculated with the assumptions explained for the
calculation of inhabitants served indicator.
90 5. Analysis of the Results
FIGURE 50: CLOSEST FACILITY ROUTE FOR STOP NUMBER 73376
Stop Number Potential Accessibility (s)
76895 247,5
73354 246,76
73376 237,46
71359 272,42
71267 274,89
TABLE 21: EXAMPLES OF HIGH POTENTIAL ACCESSIBILITY
As can be seen in the table 20, the values of the potential accessibility for the best cases are more
or less similar, varying from 237 to 274. As shown in the previous chapter, all these stops
present good values of connectivity and they are situated in high populated areas. So these
results are reliable, since the variation is only caused by slighty difference in values of
pedestrian catchment area and inhabitants served. In particular, the less expensive in terms of
time is the stop located in piazza Bologna, that is the stop with the highest PCA value among
the five here considered. The figure 48 clearly shows the high connectivity of the area,
characterized by an high number of intersections, allowing the pedestrians to choose different
paths in order to minimize the distance and the cost. Moreover the zone is the focal point of all
the neighborhood with an high population density, well distributed over the buildings.
91 5. Analysis of the Results
FIGURE 51: CLOSEST FACILITY ROUTE FOR STOP NUMBER 72488
On the contrary, the results obtained for the less accessible stops are not as reliable as the ones
explained above. They are characterized by an high variance, mainly caused by a not equal
ditribution of the population and of the buildings. First of all, the number of different routes is
low, due to the low connectivity of the network, forcing the users to follow precise paths: there
are few intersections but several cul de sacs.
Stop Number Potential Accessibility (s)
74169 159,21
72507 380,00
72488 401,63
72490 217,79
TABLE 22: EXAMPLES OF LOW POTENTIAL ACCESSIBILITY
The table shows the results from the less accessible stops: while the central ones reveal higher
costs, the first and the last cases reflect some particular situations.
Stop number 74169 is characterized by an inadequate and low distribution of population, the
buildings hosting many people are located close to the bus stop and they have a strong impact
in the overall evaluation due to their weight. This means that the position of the bus stop is
appropriate.
The Pedestrian Catchment Area of the stop number 72490 is really low, meaning an insufficient
connectivity. This is the reason why the cost distance is this cheap, since the service area is low
and characterized by dead ends, the pedestrian environment does not allow long routes.
These results means that generally the potential accessibility indicator reveals reliable results
well describing the connectivity of the road network according to the population served,
however some strange cases can appear as the results of extreme urban situations,
recommending to focus on them case by case.
92 6. Conclusion
6. CONCLUSION
The work confirms the strict relation between walking environment and urban structure and
planning. In the last two decades the attention to pedestrians and their relation with transit
service has increased in order to promote the concept of sustainable city. The XX century has
been characterized by an incredible development from the mechanical and informatic point of
view, leading to different needs and ways of living basically focused on fast processes to obtain
immediate results. This need of velocity can be highlighted in different fields, artistic, social and
even political, however, according to what concerns this work, the attention is moved to the
motor vehicle urban planning development. As already said, the road network constitutes a
focal point in the urban planning and it defines several characteristics concerning the
connectivity and the accessibility of the city. More over the relation between pedestrian
environment and motor vehicle oriented cities is particularly thorny.
However, nowadays policies are oriented towards a modern and sustainable urban
development and planning, giving more attention to transit services and walkable paths. These
two concepts are strictly interconnected, since every transit user starts and ends its trip walking.
The work takes into account all these concepts focusing on the interconnection between transit
service and pedestrian environemnt.
In particular, in the city of Rome the matter of public transport is actual, trying to improve its
efficiency in order to shift users from private car to transit. The work evaluates the accessibility
of the bus and tram stops for an area comprehending Nomentano district and its surroundings.
An accessibility index is proposed and the results reveal interesting considerations about its
reliability and outcomes. It takes into account three main fields through 7 total indicators: urban
network, transit efficiency and furniture of the bus stops. This choice has been made to include
the concepts characterizing and influencing the accessibility and the walkability introduced in
the second chapter.
The innovative aspect of the work is, infact, that it considers and evaluates the accessibility of
the transit stops starting from objective data reguarding different fields and merging them into
an overall singular analysis through a GIS software.
A fundamental strong point of the index is its flexibility and adaptability. The work uses the
Pairwise Comparison weighing method, fed by a questionnaire provided to 41 experts in
transportation field, in order to obtain more objective results and evaluations. However, thanks
to the choice of the indicators touching different fields, it is possible to obtain other answers
according to the specific gooal of the project. This characteristic is important, since the
93 6. Conclusion
walkability concept ranges upon several matters. For example, if a work focuses on equity,
particular importance should be given to the level of comfort and level of service provided to
the user.
The project uses then a multicriteria analysis in order to obtain a final more impacting value of
the accessibility of the stop.
The results shows immediately which zones are more accessible for pedestrians and which ones
are more users hostile. The less accessible are usually suburban areas, with low population and
road connectivity and also inadequate furniture, often with only one serving bus line and
without any waiting space available. They constitute extreme situations. Most accessible stops
are located along the main arterials of the district or in particular important social points,
remarking the importance of the urban network. However, even if the final single value can be
useful for understanding the accessibility of the stop, for a better analysis and for
understanding how to improve it, it is suitable to take into account the specific indicators.
Finally, the results seem reliable and in accordance with the characteristics of the neighborhood.
6.1 FURTHER IMPROVEMENTS AND FUTURE RESEARCH
Even if the results of the work are satisfying and they describe in an appropriate way the
accessibility of the stops, the index has some limitations and can be improved.
First of all several assumptions have been made, mainly due to the lack of available data. For
example the number of inhabitants per building could be calculated in a more precise way with
more informations and licences available. Another improvement can be brought by the
increasing of the typologies of buildings in evaluating the land use entropy. However, the
strongest assumption made is about the road network: the sidewalks and the pedestrian routes
are considered coincident with the road axis. This generally could be reliable in urban
environments, but some specific considerations should be made about the intersections,
moreover in the suburbs the assumption does not reflect the reality, especially for safety and
comfort considerations.
The work considers seven indicators, chosen through a deep analysis of the literature and in
relation with the data and the time available. The argument of the accessibility and of the
pedestrians is actual and there are a lot of studies and research about it. This provide a wide
range of indicators available to improve and extend the index of this work, according to the
data available. In particular it should be suitable to make some considerations about quality
indicators, that can better describe the willingness and the preferences of the users. This
requests more time, since this kind of information is difficult to gain, for example through a
large number of questionnaires.
94 6. Conclusion
Planning for local walkability is a increasing studied area in the last decades, thanks to the new
orientation of the policies towards the development of the sustainable city. Also for the index
presented in this work, the weaknesses explained above can be the starting point for a
significant improvement.
APPENDIX A: BUS AND TRAM LINES
Line Frequency
(n/min)
Frequency
(n/h)
Mode
111 0,0500 3 bus
135 0,0500 3 bus
16 0,0667 4 bus
163 0,1167 7 bus
168 0,0333 2 bus
19 0,1000 6 tram
211 0,0667 4 bus
223 0,0500 3 bus
235 0,0667 4 bus
3 0,1167 7 tram
309 0,1000 6 bus
310 0,1000 6 bus
338 0,0500 3 bus
351 0,0667 4 bus
360 0,1000 6 bus
38 0,0667 4 bus
409 0,1333 8 bus
441 0,0333 2 bus
445 0,0667 4 bus
448 0,0667 4 bus
450 0,0333 2 bus
490 0,1000 6 bus
492 0,0833 5 bus
495 0,0667 4 bus
53 0,0500 3 bus
542 0,1000 6 bus
544 0,0667 4 bus
545 0,0667 4 bus
548 0,0667 4 bus
60 0,1667 10 bus
61 0,0833 5 bus
62 0,0667 4 bus
63 0,0833 5 bus
649 0,0833 5 bus
66 0,0500 3 bus
71 0,0833 5 bus
75 0,0833 5 bus
80 0,1500 9 bus
Line Frequency
(n/min)
Frequency
(n/h)
Mode
83 0,0667 4 bus
88 0,0667 4 bus
89 0,0500 3 bus
90 0,1500 9 bus
910 0,0833 5 bus
92 0,0667 4 bus
APPENDIX B: LIST OF THE STOPS
Stop Code Stop Name Serving Lines
71345 VERANO/DE LOLLIS 3,19
71285 VERANO/DE LOLLIS 3,19,71
70732 DE LOLLIS/VERANO 492
74415 VERANO 71, 492
71280 VERANO 3,19, 71,448
78810 VERANO 88, 545
73417 VERANO 163
81915 VERANO 542
71351 VERANO 3,19
70731 TIBURTINA/CASTRO
LAURENZIANO 163,448,492,545,71
70824 TIBURTINA/CASTRO
LAURENZIANO 163,448,492,545,71
70441 VOLTURNO/GAETA 16, 223, 360, 38, 492, 66, 92
73368 UNIVERSITA'/SCIENZE 310
70294 INDIPENDENZA 75
71353 UNIVERSITA' LA SAPIENZA 19, 3, 88
71279 UNIVERSITA' LA SAPIENZA 19, 3, 88
70704 INDIPENDENZA 310, 492, 649
73366 INDIPENDENZA 310, 492, 649
70569 POLICLINICO/V.LE UNIVERSITA' 649
71185 VOLTURNO/CERNAIA 16, 492
70239 INDIPENDENZA 223, 360, 38, 92
74036 PORTONACCIO/RIMESSA ATAC 409, 545
70443 VOLTURNO/CERNAIA 16, 223, 360, 38, 492, 66, 90, 92
73998 PORTONACCIO/RIMESSA ATAC 409, 545
73370 UNIVERSITA'/REGINA ELENA 310
70567 S. M. BATTAGLIA (MB) 310, 492, 649
73359 UNIVERSITA'/REGINA ELENA 310
72092 REGINA ELENA/V.LE UNIVERSITA' 19, 3, 88
70702 S. M. BATTAGLIA (MB) 310, 492, 649
71277 REGINA ELENA/V.LE UNIVERSITA' 19, 3, 88
70729 TIBURTINA/VALERIO MASSIMO 163, 448, 492, 545, 71
70579 TIBURTINA/VALERIO MASSIMO 163, 448, 490, 492, 495, 545, 649, 71
77255 PROVINCIE/VALERIO MASSIMO 542
70570 POLICLINICO (H) 649
73358 IPPOCRATE/MARCHIAFAVA 310
76921 POLICLINICO (H) 649
81728 PORTONACCIO/TIBURTINA 409, 545
70577 PROVINCIE/VALERIO MASSIMO 490, 495, 542, 649
Stop Code Stop Name Serving Lines
70445 GOITO/XX SETTEMBRE 223, 360, 38, 92
73372 IPPOCRATE/MARCHIAFAVA 310
71355 POLICLINICO (H) 19, 3, 88
71275 POLICLINICO (H) 19, 3, 88
70238 PIAVE/XX SETTEMBRE 223, 360, 38, 92
74037 PORTONACCIO/TIBURTINA 409, 545
73580 QUINTINO SELLA 910
70581 TIBURTINA/CROCIATE 163, 448, 490, 492, 495, 545, 649, 71
70110 PALESTRO 16, 492, 61, 62, 66, 82
71108 TIBURTINA/VERANO 111, 211, 309, 409, 441, 545
73344 TIBURTINA/CROCIATE 163, 448, 492, 545, 71
70112 CROCE ROSSA 490, 495, 61
20194 TIBURTINA/PORTONACCIO 111, 163, 211, 309, 441, 448
20305 TIBURTINA/PORTONACCIO 111, 163, 211, 309, 441, 448
71956 TIBURTINA/VERANO 163, 448
74421 TIBURTINA/VERANO 490, 492, 495, 545, 649, 71
70135 CROCE ROSSA 490, 495, 61
82572 LEGA LOMBARDA 111, 163, 211, 309, 409, 441, 448, 490, 495, 545, 649
70111 PORTA PIA 490, 495, 61
70575 PROVINCIE 490, 495, 542, 649
80164 POLICLINICO/CASTRO PRETORIO 490, 495
74304 CROCIATE 490, 495, 649
80505 POLICLINICO/MORGAGNI 490, 495, 61
73374 IPPOCRATE/PROVINCIE 310
70133 POLICLINICO (H) 490, 495, 61
70136 PORTA PIA 61
20357 STAZ.NE TIBURTINA (MB) 441, 71
73356 IPPOCRATE/PROVINCIE 310
80437 LEGA LOMBARDA/PROVINCIE 490, 495, 649
75617 PIAVE/BELISARIO 223, 360, 38, 92
71501 PORTA PIA 82
72091 V.LE REGINA
MARGHERITA/MORGAGNI 19, 3, 88
72387 PORTA PIA 60, 62, 66, 90
76895 STAZ.NE TIBURTINA (MB) 409
81253 STAZ.NE TIBURTINA (MB) 211
80501 MORGAGNI/REGINA
MARGHERITA 490, 495, 61, 649
82005 STAZ.NE TIBURTINA (MB) 168
82006 STAZ.NE TIBURTINA (MB) 495
82007 STAZ.NE TIBURTINA (MB) 490
76908 STAZ.NE TIBURTINA (MB) 62
70123 STAZ.NE TIBURTINA (MB) 135, 163, 309, 441
Stop Code Stop Name Serving Lines
80499 MORGAGNI/REGINA
MARGHERITA 490, 495, 61, 649
82023 STAZ.NE TIBURTINA (MB) 111, 309, 409, 441, 448, 545, 71
80471 CATANIA/CREMONA 490, 495, 649
80474 CATANIA/PAVIA 490, 495, 649
71274 V.LE REGINA
MARGHERITA/MORGAGNI 19, 3, 88
71555 PORTA PIA 60, 62, 66,82, 90
71406 CALABRIA 490, 495, 89
74307 C.SO D'ITALIA/PORTA PIA 490, 495
74299 C.SO D'ITALIA/P.ZA FIUME 490, 495
72480 STAZ.NE TIBURTINA (MB) 135
73814 STAZ.NE TIBURTINA (MB) 649
80473 CATANIA/FORLI' 490, 495, 649
74300 BARI/SALERNO 61
80498 BARI/SALERNO 490, 495, 61, 649
80472 CATANIA/LECCE 490, 495, 649
80500 BARI/COMO 490, 495, 649
73624 FIUME 63, 83, 92
73375 PROVINCIE/PADOVA 310, 542
71272 REGINA MARGHERITA/GALENO 19, 3, 88,
73354 PROVINCIE/PADOVA 310, 542
70446 FIUME 223, 360
70122 LORENZO IL
MAGNIFICO/TEODORICO 168, 309, 62
74308 FIUME 490, 495
72090 REGINA MARGHERITA/GALENO 19, 3, 88
78324 LORENZO IL MAGNIFICO/PULCI 168, 309, 62
72661 FIUME 38, 80, 89
78396 CATANZARO 61
76852 VITERBO 38, 80, 89
70237 SALARIA/ANIENE 223, 360, 53, 63, 83, 910, 92
70128 CATANZARO 61
70121 LORENZO IL MAGNIFICO/G.DA
PROCIDA 168, 309, 62
82386 STAZ.NE TIBURTINA (MB)/PLE EST 548
73353 PROVINCIE/REGGIO CALABRIA 310, 542
78325 LORENZO IL MAGNIFICO/G.DA
PROCIDA 168, 309, 62
71270 V.LE REGINA
MARGHERITA/NOMENTANA 19, 3, 88
71502 NOMENTANA/REGINA
MARGHERITA 60, 62, 66, 82, 90
72089 V.LE REGINA
MARGHERITA/NOMENTANA 19, 3, 88
76991 NIZZA/MANTOVA 38, 89
Stop Code Stop Name Serving Lines
70118 RAVENNA/PIAZZA 61, 62
73376 BOLOGNA (MB) 310, 542
70120 LORENZO IL
MAGNIFICO/STAMIRA 168, 309, 62
70127 RAVENNA/PIAZZA 61, 62
71572 NOMENTANA/REGINA
MARGHERITA 60, 62, 66, 82, 90
74009 RAVENNA/VILLA MASSIMO 62
78172 LORENZO IL MAGNIFICO 168, 309, 62
82336 BOLOGNA 445
74084 TORLONIA/VILLA MASSIMO 62
72664 NIZZA/V.LE REGINA MARGHERITA 38, 89
78430 BOLOGNA (MB) 61
71404 NIZZA/V.LE REGINA MARGHERITA 38, 80, 89
70126 BOLOGNA (MB) 61
73378 XXI APRILE/VILLA RICOTTI 168, 309, 310, 445, 542
71359 V.LE REGINA MARGHERITA/NIZZA 19, 3
71553 NOMENTANA/TRIESTE 62, 66, 82
71267 V.LE REGINA MARGHERITA/NIZZA 19, 3
72042 XXI APRILE/RICOTTI (MB) 168, 309, 310, 445, 542
71503 NOMENTANA/TRIESTE 62, 66, 82
72666 DALMAZIA 38, 80, 88, 89
73951 LIVORNO 61
72655 DALMAZIA 38, 88, 89
74007 TORLONIA/NOMENTANA 62
74016 TORLONIA/NOMENTANA 62
73987 XXI APRILE/VILLA RICOTTI 309
73940 LIVORNO 61
71552 NOMENTANA/VILLA TORLONIA 60, 66, 82
71504 NOMENTANA/VILLA TORLONIA 60, 66, 82
73952 MARSICA 61
73937 MARSICA 61
72667 TRIESTE/TRENTO 38, 88, 89
72654 TRIESTE/TRENTO 38, 88, 89
20217 XXI APRILE/NARDINI 168, 310, 445, 542
73956 MASSA CARRARA 61
73351 XXI APRILE/NARDINI 168, 310, 445, 542
71551 NOMENTANA/GORIZIA 66, 82
71573 NOMENTANA/GORIZIA 66, 82
79930 GIORGI 61
73934 ARMELLINI 61
72720 TRIESTE/TRASIMENO 38, 80, 88, 89
72653 TRIESTE/TRASIMENO 38, 80, 88, 89
Stop Code Stop Name Serving Lines
72669 TRIESTE/GORIZIA 168, 38, 88, 89
71550 NOMENTANA/XXI APRILE 60, 66, 82, 90
78167 C.NE NOMENTANA 61
72652 TRIESTE/GORIZIA 38, 88, 89
72040 XXI APRILE/NOMENTANA 168, 310, 544
82253 VENUTI/XXI APRILE 544
71505 NOMENTANA/XXI APRILE 60, 66, 82, 90
81667 LANCIANI/BOLDETTI 445, 542, 544, 61
74202 LANCIANI/DE PETRA 445, 542, 544, 61
73925 MONTI TIBURTINI/C.NE
NOMENTANA 445, 542, 544, 61
74177 LANCIANI/WINCKELMANN 445, 542, 544
82251 LGO LANCIANI 445, 542, 544
73380 S. COSTANZA/NOMENTANA 168, 310, 544
72039 S. COSTANZA 168, 310
74165 CURIONI/COLLINA LANCIANI 445
78199 CARACI/M.I.T. 445
72670 TRIESTE/BELLINZONA 168, 38, 88, 89
74207 CURIONI/PENTA 445
74204 MONTI DI PIETRALATA 445
71506 NOMENTANA/S. AGNESE 66, 82
73381 S. COSTANZA/ISTRIA 168, 235, 310, 544
76614 ISTRIA 168, 310
71549 NOMENTANA/S. AGNESE 66, 82
82252 BRESSANONE (MB1) 235
72673 TRIESTE/ISTRIA 235, 38, 544, 80, 88, 89
74171 MONTI DI PIETRALATA 445
82186 BRESSANONE (MB1) 544
72651 TRIESTE/ISTRIA 38,80,88,89
74206 CURIONI/DE LORENZO 445
74169 CURIONI/REPOSSI 445
72650 S. AGNESE/ANNIBALIANO (MB1) 38, 80, 88, 89
81933 S.AGNESE/ANNIBALIANO (MB1) 235, 89
71507 NOMENTANA/ASMARA 60, 66, 82, 90
81932 S.AGNESE/ANNIBALIANO (MB1) 235, 89
72677 S. AGNESE/ANNIBALIANO (MB1) 38, 80, 88
71548 NOMENTANA/ASMARA 60, 66, 82, 90
77320 ASMARA/ADIGRAT 235, 89
77315 ASMARA/ADIGRAT 235, 89
72649 ERITREA/LAGO LESINA 38, 88
72678 ERITREA/LAGO LESINA 38, 88
72507 PIETRALATA/MONTI PIETRALATA 211
72506 PIETRALATA/VIGNA MANGANI 211
Stop Code Stop Name Serving Lines
72489 PIETRALATA/VIGNA MANGANI 211
81934 MAKALLE' 89
77869 MAKALLE' 235
71546 BATTERIA NOMENTANA 60, 66, 82, 90
77323 TRIPOLI 235, 89
77851 TRIPOLI 235
71508 BATTERIA NOMENTANA 60, 66, 82, 90
77913 VAL BREMBANA 211
72490 BENCIVENGA/VAL BREMBANA 211
77912 TEMBIEN 211, 351
77911 ETIOPIA/ADUA 135, 351
81987 ETIOPIA/ADUA 351
71509 NOMENTANA/VAL D'AOSTA 351, 60, 66, 90
72559 NOMENTANA/VAL D'AOSTA 211, 351, 60, 66, 90
71569 VAL D'AOSTA/VALSUGANA 82
78778 ADDIS ABEBA 135, 351
72491 BENCIVENGA/NOMENTANA 211
72007 ADDIS ABEBA 135, 351
81950 VAL D'AOSTA/STAZ.NE
NOMENTANA 338
82304 VAL DI FIEMME 338, 82
71568 VAL D'AOSTA/STAZ.NE
NOMENTANA 82
71567 CAMPI FLEGREI 338, 82
77592 VALDINIEVOLE 338, 82
71510 NOMENTANA/VAL D'OSSOLA 211, 338, 351, 60, 66, 82, 90
71544 NOMENTANA/VAL D'OSSOLA 211, 338, 351, 60, 66, 82, 90
APPENDIX C: LAND USE ENTROPY CALCULATION
Residential Public Commercial Industrial Total
Land-use
entropy
Line 111 Number 1460,00 2,00 1851,00 76,00 3389 0,56
Proportion 0,43 0,00 0,55 0,02
LN -0,84 -7,44 -0,60 -3,80
Line 135 Number 1497,00 11,00 1878,00 68,00 3454 0,57
Proportion 0,43 0,00 0,54 0,02
LN -0,84 -5,75 -0,61 -3,93
Line16 Number 3612,00 161,00 5665,00 51,00 9489 0,56
Proportion 0,38 0,02 0,60 0,01
LN -0,97 -4,08 -0,52 -5,23
Line163 Number 1689,00 31,00 2261,00 195,00 4176 0,63
Proportion 0,40 0,01 0,54 0,05
LN -0,91 -4,90 -0,61 -3,06
Line168 Number 3542,00 102,00 2495,00 23,00 6162 0,56
Proportion 0,57 0,02 0,40 0,00
LN -0,55 -4,10 -0,90 -5,59
Line 19 Number 6517,00 253,00 8004,00 92,00 14866 0,57
Proportion 0,44 0,02 0,54 0,01
LN -0,82 -4,07 -0,62 -5,09
Line 211 Number 2878,00 15,00 1877,00 188,00 4958 0,60
Proportion 0,58 0,00 0,38 0,04
LN -0,54 -5,80 -0,97 -3,27
Line 223 Number 4022,00 74,00 3256,00 26,00 7378 0,55
Proportion 0,55 0,01 0,44 0,00
LN -0,61 -4,60 -0,82 -5,65
Line 235 Number 3107,00 38,00 18,00 1933,00 5096 0,52
Proportion 0,61 0,01 0,00 0,38
LN -0,49 -4,90 -5,65 -0,97
Line 3 Number 4281,00 288,00 3543,00 40,00 8152 0,61
Proportion 0,53 0,04 0,43 0,00
LN -0,64 -3,34 -0,83 -5,32
Line 309 Number 3107,00 48,00 3429,00 97,00 6681 0,57
Proportion 0,47 0,01 0,51 0,01
LN -0,77 -4,94 -0,67 -4,23
Line 310 Number 3509,00 280,00 3032,00 7,00 6828 0,61
Proportion 0,51 0,04 0,44 0,00
LN -0,67 -3,19 -0,81 -6,88
Line 338 Number 2232,00 38,00 1812,00 18,00 4100 0,55
Proportion 0,54 0,01 0,44 0,00
LN -0,61 -4,68 -0,82 -5,43
Residential Public Commercial Industrial Total
Land-use
entropy
Line 351 Number 3453,00 33,00 3061,00 25,00 6572 0,54
Proportion 0,53 0,01 0,47 0,00
LN -0,64 -5,29 -0,76 -5,57
Line 360 Number 4423,00 138,00 6760,00 7,00 11328 0,53
Proportion 0,39 0,01 0,60 0,00
LN -0,94 -4,41 -0,52 -7,39
Line 38 Number 4063,00 59,00 4452,00 7,00 8581 0,53
Proportion 0,47 0,01 0,52 0,00
LN -0,75 -4,98 -0,66 -7,11
Line 409 Number 3002,00 43,00 2571,00 233,00 5849 0,63
Proportion 0,51 0,01 0,44 0,04
LN -0,67 -4,91 -0,82 -3,22
Line 441 Number 1374,00 1,00 1038,00 76,00 2489 0,58
Proportion 0,55 0,00 0,42 0,03
LN -0,59 -7,82 -0,87 -3,49
Line 445 Number 1219,00 26,00 912,00 4,00 2161 0,54
Proportion 0,56 0,01 0,42 0,00
LN -0,57 -4,42 -0,86 -6,29
Line 448 Number 1377,00 43,00 1487,00 133,00 3040 0,65
Proportion 0,45 0,01 0,49 0,04
LN -0,79 -4,26 -0,72 -3,13
Line 450 Number 5865,00 110,00 6938,00 105,00 13018 0,56
Proportion 0,45 0,01 0,53 0,01
LN -0,80 -4,77 -0,63 -4,82
Line490 Number 4547,00 201,00 5513,00 47,00 10308 0,57
Proportion 0,44 0,02 0,53 0,00
LN -0,82 -3,94 -0,63 -5,39
Line 492 Number 4665,00 332,00 9531,00 63,00 14591 0,54
Proportion 0,32 0,02 0,65 0,00
LN -1,14 -3,78 -0,43 -5,45
Line 495 Number 3853,00 163,00 4779,00 59,00 8854 0,58
Proportion 0,44 0,02 0,54 0,01
LN -0,83 -3,99 -0,62 -5,01
Line 53 Number 2858,00 103,00 4031,00 5,00 6997 0,54
Proportion 0,41 0,01 0,58 0,00
LN -0,90 -4,22 -0,55 -7,24
Line 542 Number 4290,00 105,00 3930,00 64,00 8389 0,57
Proportion 0,51 0,01 0,47 0,01
LN -0,67 -4,38 -0,76 -4,88
Line 544 Number 3947,00 88,00 3530,00 61,00 7626 0,57
Proportion 0,52 0,01 0,46 0,01
LN -0,66 -4,46 -0,77 -4,83
Residential Public Commercial Industrial Total
Land-use
entropy
Line 545 Number 1885,00 66,00 1879,00 293,00 4123 0,70
Proportion 0,46 0,02 0,46 0,07
LN -0,78 -4,13 -0,79 -2,64
Line 548 Number 3946,00 88,00 3357,00 34,00 7425 0,56
Proportion 0,53 0,01 0,45 0,00
LN -0,63 -4,44 -0,79 -5,39
Line 60 Number 3353,00 191,00 4229,00 12,00 7785 0,57
Proportion 0,43 0,02 0,54 0,00
LN -0,84 -3,71 -0,61 -6,48
Line 61 Number 3826,00 173,00 4273,00 45,00 8317 0,58
Proportion 0,46 0,02 0,51 0,01
LN -0,78 -3,87 -0,67 -5,22
Line 62 Number 4310,00 289,00 8693,00 3,00 13295 0,53
Proportion 0,32 0,02 0,65 0,00
LN -1,13 -3,83 -0,42 -8,40
Line 63 Number 6278,00 232,00 7679,00 3,00 14192 0,55
Proportion 0,44 0,02 0,54 0,00
LN -0,82 -4,11 -0,61 -8,46
Line 649 Number 4019,00 174,00 5593,00 46,00 9832 0,56
Proportion 0,41 0,02 0,57 0,00
LN -0,89 -4,03 -0,56 -5,36
Line 66 Number 3813,00 141,00 4963,00 9,00 8926 0,55
Proportion 0,43 0,02 0,56 0,00
LN -0,85 -4,15 -0,59 -6,90
Line 71 Number 2576,00 122,00 6830,00 67,00 9595 0,49
Proportion 0,27 0,01 0,71 0,01
LN -1,32 -4,36 -0,34 -4,96
Line 75 Number 3335,00 258,00 4821,00 9,00 8423 0,58
Proportion 0,40 0,03 0,57 0,00
LN -0,93 -3,49 -0,56 -6,84
Line 80 Number 3844,00 137,00 5013,00 2,00 8996 0,54
Proportion 0,43 0,02 0,56 0,00
LN -0,85 -4,18 -0,58 -8,41
Line 82 Number 2962,00 132,00 4470,00 7,00 7571 0,54
Proportion 0,39 0,02 0,59 0,00
LN -0,94 -4,05 -0,53 -6,99
Line 83 Number 5854,00 237,00 7821,00 16,00 13928 0,55
Proportion 0,42 0,02 0,56 0,00
LN -0,87 -4,07 -0,58 -6,77
Line 88 Number 4462,00 244,00 2539,00 6,00 7251 0,57
Proportion 0,62 0,03 0,35 0,00
LN -0,49 -3,39 -1,05 -7,10
Residential Public Commercial Industrial Total
Land-use
entropy
Line 89 Number 3563,00 78,00 3688,00 5,00 7334 0,54
Proportion 0,49 0,01 0,50 0,00
LN -0,72 -4,54 -0,69 -7,29
Line 90 Number 2947,00 100,00 3366,00 12,00 6425 0,56
Proportion 0,46 0,02 0,52 0,00
LN -0,78 -4,16 -0,65 -6,28
Line 910 Number 2643,00 108,00 3609,00 21,00 6381 0,56
Proportion 0,41 0,02 0,57 0,00
LN -0,88 -4,08 -0,57 -5,72
Line 92 Number 3673,00 58,00 4156,00 17,00 7904 0,54
Proportion 0,46 0,01 0,53 0,00
LN -0,77 -4,91 -0,64 -6,14
APPENDIX D: LIST OF THE INDICATORS FOR EACH STOP
N_lines Frequency
avg
Land-use
entropy avg
LOS PCA Inhab_served Comfort
71345 2 0,108 0,592 5 0,363 1093 2
71285 3 0,100 0,559 5 0,346 1039 2
70732 1 0,083 0,543 5 0,335 1187 1
74415 2 0,083 0,518 5 0,393 1111 4
71280 4 0,092 0,583 5 0,371 1086 2
78810 2 0,067 0,633 5 0,371 1086 2
73417 1 0,117 0,633 5 0,371 1086 2
81915 1 0,100 0,570 5 0,378 985 2
71351 2 0,108 0,592 5 0,385 1008 2
70731 5 0,083 0,605 4 0,356 835 5
70824 5 0,083 0,605 4 0,311 745 5
70441 7 0,069 0,542 3 0,522 2978 1
73368 1 0,100 0,606 5 0,480 1458 1
70294 1 0,083 0,577 5 0,540 4721 2
71353 3 0,094 0,583 4 0,325 810 2
71279 3 0,094 0,583 4 0,347 832 2
70704 3 0,089 0,571 5 0,577 5086 2
73366 3 0,089 0,571 5 0,558 5191 2
70569 1 0,083 0,565 5 0,454 1200 2
71185 2 0,075 0,550 5 0,487 3058 1
70239 4 0,071 0,535 5 0,554 4778 1
74036 2 0,100 0,663 4 0,278 290 1
70443 8 0,079 0,544 5 0,478 3107 1
73998 2 0,100 0,663 4 0,270 263 1
73370 1 0,100 0,606 5 0,466 1564 4
70567 3 0,089 0,571 5 0,488 3931 1
73359 1 0,100 0,606 5 0,452 1597 4
72092 3 0,094 0,583 4 0,530 1990 2
70702 3 0,089 0,571 5 0,448 3180 1
71277 3 0,094 0,583 4 0,472 1524 2
70729 5 0,083 0,605 5 0,314 643 5
70579 8 0,083 0,593 5 0,265 301 5
77255 1 0,100 0,570 5 0,337 1051 1
70570 1 0,083 0,565 5 0,249 145 2
73358 1 0,100 0,606 5 0,540 2483 1
76921 1 0,083 0,565 5 0,253 206 2
81728 2 0,100 0,663 5 0,337 1031 4
70577 4 0,088 0,572 5 0,426 1715 1
70137 6 0,069 0,551 4 0,551 4599 2
N_lines Frequency
avg
Land-use
entropy avg
LOS PCA Inhab_served Comfort
70445 4 0,071 0,535 5 0,549 4530 1
73372 1 0,100 0,606 5 0,514 2836 1
71355 3 0,094 0,583 4 0,301 503 2
71275 3 0,094 0,583 4 0,302 589 2
70238 4 0,071 0,535 5 0,524 4341 1
74037 2 0,100 0,663 5 0,535 1919 1
73580 1 0,083 0,559 5 0,513 4709 1
70581 8 0,083 0,593 5 0,388 379 5
70110 6 0,069 0,551 3 0,493 3068 1
71108 6 0,075 0,606 5 0,436 120 1
73344 5 0,083 0,605 5 0,382 376 5
70112 3 0,083 0,579 2 0,546 2177 2
20194 6 0,072 0,600 4 0,548 3476 1
20305 6 0,072 0,600 4 0,558 3234 1
71956 2 0,092 0,643 4 0,438 205 1
74421 6 0,081 0,576 3 0,447 160 1
70135 3 0,083 0,579 4 0,533 2162 1
82572 11 0,080 0,604 3 0,478 1025 1
70111 3 0,083 0,579 5 0,559 2694 1
70575 4 0,088 0,572 5 0,541 3458 1
80164 2 0,083 0,577 5 0,423 1242 1
74304 3 0,083 0,573 3 0,390 554 1
80505 3 0,083 0,579 5 0,512 1305 1
73374 1 0,100 0,606 5 0,589 3902 1
70133 3 0,083 0,579 5 0,500 1383 1
70136 1 0,083 0,583 5 0,584 2869 2
20357 2 0,058 0,537 3 0,530 339 5
73356 1 0,100 0,606 5 0,582 3982 1
80437 3 0,083 0,573 2 0,589 3876 5
75617 4 0,071 0,535 5 0,563 5165 3
71501 1 0,067 0,545 5 0,570 2868 2
72091 3 0,094 0,583 4 0,571 1963 2
72387 4 0,108 0,552 4 0,563 2758 2
76895 1 0,133 0,626 5 0,557 395 5
81253 1 0,067 0,595 5 0,548 382 5
80501 4 0,083 0,575 3 0,592 1987 5
82005 1 0,033 0,558 5 0,542 379 5
82006 1 0,067 0,578 5 0,520 344 5
82007 1 0,100 0,575 5 0,521 344 5
76908 1 0,067 0,525 5 0,514 344 5
70123 4 0,075 0,589 5 0,528 602 5
70724 1 0,083 0,543 5 0,501 343 5
N_lines Frequency
avg
Land-use
entropy avg
LOS PCA Inhab_served Comfort
80499 4 0,083 0,575 3 0,592 2081 5
82023 7 0,076 0,599 5 0,484 291 5
80471 3 0,083 0,573 4 0,577 4624 5
80474 3 0,083 0,573 4 0,577 4654 5
71274 3 0,094 0,583 3 0,559 1906 2
71555 5 0,100 0,550 5 0,549 3032 1
71406 3 0,072 0,565 5 0,596 5419 3
74307 2 0,083 0,577 2 0,626 4696 2
74299 2 0,083 0,577 2 0,602 4847 2
72480 1 0,050 0,569 5 0,505 803 5
73814 1 0,083 0,565 5 0,512 886 5
80473 3 0,083 0,573 5 0,574 3862 5
74300 1 0,083 0,583 5 0,637 2670 1
80498 4 0,083 0,575 2 0,645 2649 5
80472 3 0,083 0,573 5 0,587 3917 5
80500 3 0,083 0,573 5 0,610 2801 2
73624 3 0,072 0,546 5 0,656 5653 2
73375 2 0,100 0,588 5 0,572 4613 1
71272 3 0,094 0,583 5 0,679 2496 5
73354 2 0,100 0,588 5 0,575 4667 4
70446 2 0,075 0,538 5 0,648 5578 2
70122 3 0,067 0,552 5 0,522 1665 1
74308 2 0,083 0,577 5 0,662 5840 1
72090 3 0,094 0,583 5 0,635 2470 5
78324 3 0,067 0,552 5 0,540 2311 1
72661 3 0,089 0,538 5 0,608 5290 1
78396 1 0,083 0,583 5 0,601 3882 4
76852 3 0,089 0,538 5 0,594 4971 1
70237 7 0,071 0,545 3 0,628 5057 1
70128 1 0,083 0,583 5 0,535 3589 4
70121 3 0,067 0,552 5 0,544 3430 4
82386 1 0,067 0,557 5 0,234 14 5
73353 2 0,100 0,588 5 0,546 4854 1
78325 3 0,067 0,552 5 0,520 3437 1
71270 3 0,094 0,583 5 0,595 3682 2
71502 5 0,100 0,550 4 0,568 3856 5
72089 3 0,094 0,583 5 0,602 4024 2
76991 2 0,058 0,535 5 0,505 4758 1
72662 2 0,058 0,535 5 0,523 5111 1
70118 2 0,075 0,554 5 0,592 4778 1
73376 2 0,100 0,588 5 0,638 5230 4
70120 3 0,067 0,552 5 0,558 4453 1
N_lines Frequency
avg
Land-use
entropy avg
LOS PCA Inhab_served Comfort
70127 2 0,075 0,554 5 0,592 4735 1
71572 5 0,100 0,550 4 0,555 3802 3
74009 1 0,067 0,525 5 0,553 3315 1
78172 3 0,067 0,552 5 0,615 4748 1
82336 1 0,067 0,542 5 0,671 5151 1
74084 1 0,067 0,525 5 0,480 2480 1
72664 2 0,058 0,535 5 0,557 4698 1
78430 1 0,083 0,583 5 0,572 4267 1
71404 3 0,089 0,538 5 0,569 4883 1
70126 1 0,083 0,583 5 0,545 4034 1
73378 5 0,080 0,570 5 0,605 4325 3
71359 2 0,108 0,592 5 0,586 5197 2
71553 3 0,061 0,540 5 0,451 2295 5
71267 2 0,108 0,592 5 0,575 4724 2
72042 5 0,080 0,570 4 0,575 3980 3
71503 3 0,061 0,540 5 0,415 1965 4
72666 4 0,083 0,546 3 0,597 4986 1
73951 1 0,083 0,583 5 0,551 3488 1
72655 3 0,061 0,546 5 0,567 4431 1
74007 1 0,067 0,525 5 0,521 2022 1
74016 1 0,067 0,525 5 0,521 1975 1
73987 1 0,100 0,574 5 0,519 2802 1
73940 1 0,083 0,583 5 0,522 3278 4
71552 3 0,094 0,556 5 0,581 1996 5
71504 3 0,094 0,556 5 0,558 2052 5
73952 1 0,083 0,583 5 0,443 2799 4
73937 1 0,083 0,583 5 0,486 3049 4
72667 3 0,061 0,546 5 0,492 2674 4
72654 3 0,061 0,546 5 0,490 2623 4
20217 4 0,075 0,569 5 0,557 2782 1
73956 1 0,083 0,583 5 0,529 3365 1
73351 4 0,075 0,569 5 0,532 2644 1
71551 2 0,058 0,547 5 0,558 1953 5
71573 2 0,058 0,547 5 0,521 1667 5
79930 1 0,083 0,583 5 0,522 3777 1
73934 1 0,083 0,583 5 0,490 3434 1
72720 4 0,083 0,546 5 0,493 2630 4
72653 4 0,083 0,546 5 0,493 2600 1
73931 1 0,083 0,583 5 0,478 2949 1
72669 4 0,054 0,549 5 0,579 3819 4
71550 4 0,108 0,556 5 0,514 1350 6
78167 1 0,083 0,583 5 0,554 2532 1
N_lines Frequency
avg
Land-use
entropy avg
LOS PCA Inhab_served Comfort
72652 3 0,061 0,546 5 0,573 3808 4
72040 3 0,067 0,577 5 0,570 2133 1
82253 1 0,067 0,568 5 0,524 2243 1
71505 4 0,108 0,556 5 0,550 1694 7
81667 4 0,079 0,566 5 0,460 3059 1
74202 4 0,079 0,566 5 0,513 2828 4
73925 4 0,079 0,566 3 0,510 1538 1
74177 3 0,078 0,560 5 0,452 2749 4
82251 3 0,078 0,560 5 0,584 2692 1
73380 3 0,067 0,577 5 0,574 2190 4
72039 2 0,067 0,582 5 0,528 2738 1
74165 1 0,067 0,542 5 0,261 588 1
78199 1 0,067 0,542 5 0,337 373 1
72670 4 0,054 0,549 5 0,493 3882 1
74207 1 0,067 0,542 5 0,273 622 1
74204 1 0,067 0,542 5 0,404 789 1
71506 2 0,058 0,547 5 0,498 1492 5
73381 4 0,067 0,564 4 0,528 3765 1
76614 2 0,067 0,582 5 0,511 3710 1
71549 2 0,058 0,547 5 0,531 1557 2
82252 1 0,067 0,524 5 0,462 2706 1
72673 6 0,078 0,546 5 0,503 3589 1
74171 1 0,067 0,542 5 0,382 818 4
82186 1 0,067 0,568 5 0,387 2247 1
72651 4 0,083 0,546 4 0,513 3621 1
74206 1 0,067 0,542 5 0,366 767 4
74169 1 0,067 0,542 2 0,361 765 1
72650 4 0,083 0,546 5 0,579 3999 1
81933 2 0,058 0,532 5 0,510 2909 1
71507 4 0,108 0,556 3 0,537 1565 5
81932 2 0,058 0,532 5 0,434 2485 1
72677 3 0,094 0,547 5 0,507 3723 1
71548 4 0,108 0,556 5 0,480 1463 5
77320 2 0,058 0,532 5 0,323 1630 1
77315 2 0,058 0,532 5 0,338 1583 1
72649 2 0,067 0,548 5 0,407 3086 1
72678 2 0,067 0,548 5 0,386 2975 1
72507 1 0,067 0,595 2 0,371 190 1
72506 1 0,067 0,595 5 0,385 102 4
72488 1 0,067 0,595 2 0,347 182 1
72489 1 0,067 0,595 5 0,383 106 1
81934 1 0,050 0,541 5 0,411 1837 1
N_lines Frequency
avg
Land-use
entropy avg
LOS PCA Inhab_served Comfort
77869 1 0,067 0,524 5 0,411 1908 1
71546 4 0,108 0,556 3 0,518 2830 4
77323 2 0,058 0,532 5 0,452 2626 1
77851 1 0,067 0,524 5 0,445 2237 1
71508 4 0,108 0,556 5 0,504 2863 4
77913 1 0,067 0,595 5 0,328 929 1
72490 1 0,067 0,595 2 0,268 661 1
77912 2 0,067 0,565 5 0,557 3352 1
77911 2 0,058 0,552 5 0,450 3036 1
81987 1 0,067 0,535 5 0,418 2805 1
71509 4 0,108 0,554 5 0,452 2610 5
72559 5 0,100 0,562 4 0,472 2815 2
71569 1 0,067 0,545 5 0,440 2551 1
78778 2 0,058 0,552 5 0,541 3951 4
72491 1 0,067 0,595 5 0,487 2559 1
72007 2 0,058 0,552 5 0,543 3493 1
81950 1 0,050 0,548 5 0,335 2203 4
82304 2 0,058 0,546 5 0,433 3133 1
71568 1 0,067 0,545 5 0,357 2473 4
71567 2 0,058 0,546 5 0,288 2005 1
77592 2 0,058 0,546 5 0,496 3964 1
71510 7 0,088 0,558 4 0,481 3190 4
71544 7 0,088 0,558 4 0,467 3014 4
APPENDIX E: IDEAL POINT METHOD CALCULATION
Stop Code si+ si- ci+ ci-
71345 0,2297 0,24 0,5096 0,4904
71285 0,2396 0,22 0,4757 0,5243
70732 0,2780 0,18 0,3957 0,6043
74415 0,2630 0,19 0,4193 0,5807
71280 0,2331 0,21 0,4724 0,5276
78810 0,2731 0,18 0,3982 0,6018
73417 0,2263 0,26 0,5392 0,4608
81915 0,2481 0,22 0,4683 0,5317
71351 0,2285 0,24 0,5118 0,4882
70731 0,2341 0,19 0,4453 0,5547
70824 0,2411 0,19 0,4349 0,5651
70441 0,2466 0,17 0,4149 0,5851
73368 0,2304 0,23 0,5039 0,4961
70294 0,2173 0,24 0,5246 0,4754
71353 0,2488 0,19 0,4305 0,5695
71279 0,2459 0,19 0,4347 0,5653
70704 0,1909 0,26 0,5744 0,4256
73366 0,1915 0,26 0,5730 0,4270
70569 0,2573 0,19 0,4298 0,5702
71185 0,2476 0,20 0,4439 0,5561
70239 0,2323 0,23 0,4934 0,5066
74036 0,2611 0,22 0,4566 0,5434
70443 0,2138 0,22 0,5115 0,4885
73998 0,2625 0,22 0,4551 0,5449
73370 0,2172 0,24 0,5223 0,4777
70567 0,2080 0,23 0,5275 0,4725
73359 0,2180 0,24 0,5203 0,4797
72092 0,2129 0,21 0,4978 0,5022
70702 0,2173 0,22 0,5030 0,4970
71277 0,2234 0,20 0,4745 0,5255
70729 0,2389 0,21 0,4635 0,5365
70579 0,2426 0,22 0,4717 0,5283
77255 0,2563 0,22 0,4576 0,5424
70570 0,2928 0,18 0,3808 0,6192
73358 0,2133 0,25 0,5356 0,4644
76921 0,2913 0,18 0,3821 0,6179
81728 0,2266 0,24 0,5188 0,4812
70577 0,2310 0,21 0,4713 0,5287
70137 0,2193 0,21 0,4905 0,5095
Stop Code si+ si- ci+ ci-
70445 0,2335 0,22 0,4872 0,5128
73372 0,2110 0,25 0,5386 0,4614
71355 0,2564 0,19 0,4209 0,5791
71275 0,2549 0,19 0,4226 0,5774
70238 0,2355 0,22 0,4785 0,5215
74037 0,2077 0,26 0,5539 0,4461
73580 0,2288 0,23 0,5058 0,4942
70581 0,2255 0,22 0,4953 0,5047
70110 0,2478 0,17 0,4031 0,5969
71108 0,2557 0,20 0,4350 0,5650
73344 0,2352 0,21 0,4706 0,5294
70112 0,2527 0,18 0,4109 0,5891
20194 0,2147 0,21 0,4890 0,5110
20305 0,2162 0,20 0,4854 0,5146
71956 0,2537 0,20 0,4456 0,5544
74421 0,2641 0,16 0,3819 0,6181
70135 0,2313 0,19 0,4530 0,5470
82572 0,2345 0,21 0,4748 0,5252
70111 0,2204 0,22 0,4988 0,5012
70575 0,2029 0,23 0,5336 0,4664
80164 0,2543 0,19 0,4316 0,5684
74304 0,2738 0,15 0,3545 0,6455
80505 0,2400 0,20 0,4597 0,5403
73374 0,1975 0,26 0,5725 0,4275
70133 0,2397 0,20 0,4587 0,5413
70136 0,2267 0,22 0,4968 0,5032
20357 0,3045 0,13 0,3002 0,6998
73356 0,1972 0,26 0,5730 0,4270
80437 0,2284 0,21 0,4782 0,5218
75617 0,2214 0,24 0,5151 0,4849
71501 0,2621 0,20 0,4272 0,5728
72091 0,2112 0,22 0,5057 0,4943
72387 0,1890 0,25 0,5650 0,4350
76895 0,2088 0,31 0,6008 0,3992
81253 0,2730 0,19 0,4146 0,5854
80501 0,2212 0,20 0,4705 0,5295
82005 0,3384 0,17 0,3279 0,6721
82006 0,2776 0,19 0,4009 0,5991
82007 0,2364 0,24 0,5000 0,5000
76908 0,2903 0,18 0,3818 0,6182
70123 0,2398 0,20 0,4599 0,5401
70724 0,2635 0,20 0,4340 0,5660
Stop Code si+ si- ci+ ci-
80499 0,2200 0,20 0,4728 0,5272
82023 0,2310 0,21 0,4815 0,5185
80471 0,1937 0,23 0,5461 0,4539
80474 0,1936 0,23 0,5467 0,4533
71274 0,2238 0,20 0,4747 0,5253
71555 0,1909 0,25 0,5666 0,4334
71406 0,2146 0,25 0,5333 0,4667
74307 0,2390 0,22 0,4779 0,5221
74299 0,2390 0,22 0,4766 0,5234
72480 0,3017 0,17 0,3577 0,6423
73814 0,2491 0,21 0,4536 0,5464
80473 0,1943 0,24 0,5527 0,4473
74300 0,2324 0,23 0,4964 0,5036
80498 0,2306 0,21 0,4727 0,5273
80472 0,1933 0,24 0,5562 0,4438
80500 0,2135 0,23 0,5151 0,4849
73624 0,2229 0,25 0,5327 0,4673
73375 0,1884 0,27 0,5874 0,4126
71272 0,1862 0,26 0,5816 0,4184
73354 0,1721 0,27 0,6131 0,3869
70446 0,2280 0,25 0,5260 0,4740
70122 0,2658 0,18 0,4041 0,5959
74308 0,2086 0,27 0,5656 0,4344
72090 0,1870 0,25 0,5745 0,4255
78324 0,2580 0,19 0,4213 0,5787
72661 0,2058 0,26 0,5591 0,4409
78396 0,2099 0,24 0,5340 0,4660
76852 0,2070 0,25 0,5516 0,4484
70237 0,2257 0,22 0,4982 0,5018
70128 0,2150 0,23 0,5158 0,4842
70121 0,2363 0,21 0,4648 0,5352
82386 0,3115 0,16 0,3420 0,6580
73353 0,1888 0,27 0,5872 0,4128
78325 0,2493 0,20 0,4420 0,5580
71270 0,1869 0,25 0,5741 0,4259
71502 0,1673 0,25 0,5995 0,4005
72089 0,1839 0,26 0,5824 0,4176
76991 0,2691 0,20 0,4323 0,5677
72662 0,2672 0,21 0,4437 0,5563
70118 0,2311 0,23 0,5026 0,4974
73376 0,1676 0,29 0,6314 0,3686
70120 0,2416 0,22 0,4725 0,5275
Stop Code si+ si- ci+ ci-
70127 0,2313 0,23 0,5019 0,4981
71572 0,1759 0,24 0,5802 0,4198
74009 0,2693 0,20 0,4222 0,5778
78172 0,2386 0,23 0,4894 0,5106
82336 0,2534 0,24 0,4875 0,5125
74084 0,2796 0,18 0,3886 0,6114
72664 0,2668 0,21 0,4408 0,5592
78430 0,2219 0,24 0,5171 0,4829
71404 0,2083 0,25 0,5460 0,4540
70126 0,2245 0,23 0,5076 0,4924
73378 0,1894 0,24 0,5634 0,4366
71359 0,1693 0,29 0,6327 0,3673
71553 0,2633 0,18 0,4046 0,5954
71267 0,1717 0,28 0,6237 0,3763
72042 0,1970 0,22 0,5255 0,4745
71503 0,2715 0,17 0,3825 0,6175
72666 0,2211 0,22 0,5013 0,4987
73951 0,2279 0,23 0,4974 0,5026
72655 0,2532 0,21 0,4554 0,5446
74007 0,2817 0,18 0,3882 0,6118
74016 0,2822 0,18 0,3873 0,6127
73987 0,2177 0,24 0,5251 0,4749
73940 0,2183 0,22 0,5066 0,4934
71552 0,2029 0,24 0,5408 0,4592
71504 0,2032 0,24 0,5379 0,4621
73952 0,2287 0,21 0,4805 0,5195
73937 0,2228 0,22 0,4945 0,5055
72667 0,2573 0,18 0,4167 0,5833
72654 0,2579 0,18 0,4150 0,5850
20217 0,2286 0,21 0,4779 0,5221
73956 0,2299 0,22 0,4908 0,5092
73351 0,2312 0,20 0,4696 0,5304
71551 0,2698 0,19 0,4081 0,5919
71573 0,2746 0,18 0,3942 0,6058
79930 0,2273 0,23 0,4982 0,5018
73934 0,2318 0,22 0,4849 0,5151
72720 0,2146 0,21 0,4991 0,5009
72653 0,2278 0,21 0,4786 0,5214
73931 0,2368 0,21 0,4720 0,5280
72669 0,2515 0,21 0,4507 0,5493
71550 0,1934 0,26 0,5723 0,4277
78167 0,2363 0,22 0,4779 0,5221
Stop Code si+ si- ci+ ci-
72652 0,2446 0,21 0,4597 0,5403
72040 0,2528 0,19 0,4345 0,5655
82253 0,2686 0,19 0,4080 0,5920
71505 0,1847 0,27 0,5922 0,4078
81667 0,2263 0,21 0,4769 0,5231
74202 0,2115 0,21 0,5028 0,4972
73925 0,2531 0,16 0,3914 0,6086
74177 0,2272 0,20 0,4706 0,5294
82251 0,2324 0,21 0,4774 0,5226
73380 0,2404 0,20 0,4542 0,5458
72039 0,2541 0,19 0,4321 0,5679
74165 0,3148 0,15 0,3231 0,6769
78199 0,3096 0,15 0,3301 0,6699
72670 0,2663 0,19 0,4180 0,5820
74207 0,3131 0,15 0,3248 0,6752
74204 0,2985 0,16 0,3471 0,6529
71506 0,2778 0,17 0,3857 0,6143
73381 0,2412 0,19 0,4348 0,5652
76614 0,2475 0,20 0,4504 0,5496
71549 0,2833 0,17 0,3754 0,6246
82252 0,2793 0,18 0,3894 0,6106
72673 0,2165 0,22 0,5037 0,4963
74171 0,2903 0,16 0,3594 0,6406
82186 0,2781 0,17 0,3801 0,6199
72651 0,2210 0,20 0,4784 0,5216
74206 0,2923 0,16 0,3559 0,6441
74169 0,3259 0,10 0,2286 0,7714
72650 0,2111 0,23 0,5261 0,4739
81933 0,2796 0,18 0,3893 0,6107
71507 0,2064 0,23 0,5286 0,4714
81932 0,2878 0,16 0,3635 0,6365
72677 0,2080 0,24 0,5336 0,4664
71548 0,1959 0,25 0,5632 0,4368
77320 0,3054 0,15 0,3254 0,6746
77315 0,3045 0,15 0,3268 0,6732
72649 0,2671 0,18 0,4017 0,5983
72678 0,2698 0,18 0,3953 0,6047
72507 0,3230 0,11 0,2557 0,7443
72506 0,2896 0,17 0,3715 0,6285
72488 0,3250 0,11 0,2505 0,7495
72489 0,2994 0,17 0,3557 0,6443
81934 0,3133 0,15 0,3211 0,6789
Stop Code si+ si- ci+ ci-
77869 0,2903 0,17 0,3625 0,6375
71546 0,1933 0,24 0,5487 0,4513
77323 0,2853 0,17 0,3705 0,6295
77851 0,2846 0,17 0,3760 0,6240
71508 0,1763 0,26 0,5964 0,4036
77913 0,2937 0,16 0,3579 0,6421
72490 0,3269 0,11 0,2436 0,7564
77912 0,2513 0,20 0,4448 0,5552
77911 0,2771 0,17 0,3862 0,6138
81987 0,2785 0,18 0,3863 0,6137
71509 0,1825 0,26 0,5847 0,4153
72559 0,1932 0,22 0,5371 0,4629
71569 0,2763 0,18 0,3881 0,6119
78778 0,2551 0,20 0,4427 0,5573
72491 0,2627 0,19 0,4182 0,5818
72007 0,2687 0,19 0,4163 0,5837
81950 0,3056 0,15 0,3334 0,6666
82304 0,2792 0,17 0,3828 0,6172
71568 0,2739 0,17 0,3874 0,6126
71567 0,3021 0,15 0,3323 0,6677
77592 0,2699 0,19 0,4155 0,5845
71510 0,1869 0,22 0,5401 0,4599
71544 0,1900 0,22 0,5325 0,4675
APPENDIX F: PYTHON CODE def main():
pass
if __name__ == '__main__':
main()
import csv
import time
from geopy.geocoders import Nominatim
geolocator = Nominatim()
infile1 = open('C:\GoogleMapsAPI\GoogleMapsAPI_Roma\ROMA_OSM\Comer7CSV.csv','r')
outfile1 =
open('C:\GoogleMapsAPI\GoogleMapsAPI_Roma\ROMA_OSM\ComerResultados7Def.csv','w')
reader1 = csv.reader(infile1)
writer1 = csv.writer(outfile1, delimiter=',')
origen_index=3
destino_index=4
for row in list(reader1)[0:]:
try:
Number= row[0]
Location1 = row[1]
print(Location1)
my_distance = geolocator.geocode(Location1)
lat = my_distance.latitude
lng = my_distance.longitude
writer1.writerow((Number, Location1, lat, lng))
except:
pass
time.sleep(4)
outfile1.close()
infile1.close()
LIST OF FIGURES
Figure 1: Rationale graph .................................................................................................................... 7
Figure 2: Evolution of residential street grids in the last century................................................... 10
Figure 3: Maximum Block Length vs. Block Size............................................................................. 25
Figure 4: Link node ratio ................................................................................................................... 26
Figure 5: Distance decay to metro, train and bus services (El-Geneidy, 2013) .............................. 33
Figure 6: Calculation of service areas in a straight line (circle), and through the street network
(Gutierrez, 2008) ................................................................................................................................ 34
Figure 7: Location of Nomentano district ........................................................................................ 37
Figure 8: Population of district zone 3a in 2016, by five-years age groups .................................... 38
Figure 9: Comercial activities distribution in Nomentano district ................................................. 39
Figure 10: Buffer process of Nomentano district ............................................................................. 40
Figure 11: Distribution of bus and tram stops in nomentano area ................................................. 41
Figure 12: Bus lines (green) and tram lines (red) crossing nomentano district ............................. 42
Figure 13: Conceptual map ............................................................................................................... 43
Figure 14: Hierarchical characteristics and functional links of urban roads (Regolamento viario
2015).................................................................................................................................................... 45
Figure 15: Pedestrian cathcment area (Schlossberg, 2006) .............................................................. 46
Figure 16: Example of the questionnare ........................................................................................... 54
Figure 17: Roads classification .......................................................................................................... 56
Figure 18: Dead ends tool.................................................................................................................. 57
Figure 19: Dead ends distribution .................................................................................................... 58
Figure 20: Intersections distribution ................................................................................................. 59
Figure 21: Service area solver procedure ......................................................................................... 60
Figure 22: PCA of the public tranport stops .................................................................................... 61
Figure 23: Percentage of number of lines per stop ......................................................................... 62
Figure 24: Number of lines per stop distribution ............................................................................ 63
Figure 25: Frequency of the bus stop ................................................................................................ 64
Figure 26: Feature to point process ................................................................................................... 65
Figure 27: Service areas of the two tram lines.................................................................................. 66
Figure 28: Buildings typology distribuition over line 309 service area .......................................... 66
Figure 29: LUE average per stop ...................................................................................................... 67
Figure 30: Percentage of los distribution .......................................................................................... 68
Figure 31: Pedestrian catchment areas ............................................................................................. 69
Figure 32: Comparison between high and low PCA ....................................................................... 70
Figure 33: Distribution of household's members ............................................................................. 71
Figure 34: Inhabitants distribution in nomentano district .............................................................. 72
Figure 35: Examples of inhabitants served per stop ........................................................................ 72
Figure 36: Level of comfort distribution .......................................................................................... 73
Figure 37: Frequency - comfort comparison .................................................................................... 75
Figure 38: Land use entropy - level of service comparison ............................................................. 75
Figure 39: Graphic comparison with the positive ideal stop .......................................................... 79
Figure 40: PCA of v.le Regina Margherita/Nizza stop .................................................................... 80
Figure 41: Inhabitants served by v.le Regina Margherita/Nizza stop ............................................ 80
Figure 42: PCA of Curioni/Repossi stop .......................................................................................... 81
Figure 43: Inhabitants served by Curioni/repossi stop ................................................................... 81
Figure 44: Example of LOS of d (Google maps)............................................................................... 82
Figure 45: Bus stop final accessibility ............................................................................................... 83
Figure 46: Nomentana / xxi Aprile stop ........................................................................................... 84
Figure 47: Best results graphical representation .............................................................................. 86
Figure 48: Worst results graphical representation ........................................................................... 87
Figure 49: Resistance matrix ............................................................................................................. 88
Figure 50: Closest facility route for stop number 73376 .................................................................. 90
Figure 51: Closest facility route for stop number 72488 .................................................................. 91
LIST OF TABLES
Table 1: Different groups tend to rely more on certain modes. Rating from 3 (most important) to
0 (unimportant) (litman2017) ............................................................................................................ 20
Table 2: Comparison of transportation modes (“transport diversity,” vtpi, 2006) ........................ 21
Table 3 Multi-Modal Level Of Service (“Transport Options,” VTPI 2006; FDOT 2007)............... 23
Table 4: Factors contributing to transportation disadvantaged status ........................................... 30
Table 5: Transit Oriented Versus Adjacent (Renne 2009) ................................................................ 32
Table 6: Accessibility Index ............................................................................................................... 47
Table 7: Level of service definition ................................................................................................... 49
Table 8: Road network characteristics .............................................................................................. 60
Table 9: Number of lines serving the stops ...................................................................................... 62
Table 10: Land use entropy calculation for line 309 ........................................................................ 67
Table 11: Comfort index .................................................................................................................... 73
Table 12: Comparison matrix ............................................................................................................ 75
Table 13: Determining the Relative Criterion Weights ................................................................... 76
Table 14: Ranking of the indicators according to their weight ....................................................... 77
Table 15: Determing the Consistency Ratio ..................................................................................... 77
Table 16: Positive and negative ideal point values .......................................................................... 78
Table 17: Weighted standardized values for the best and the worst stop ..................................... 79
Table 18: Best results indicators values ............................................................................................ 85
Table 19: Worst results indicators values ......................................................................................... 87
Table 20: Examples of cost distance function ................................................................................... 89
Table 21: Examples of high potential accessibility ......................................................................... 90
Table 22: Examples of low potential accessibility ............................................................................ 91
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ACKNOWLEDGEMENTS
First of all I want to thank Prof. Corazza for her availability and kindness in supervising me in
this work, giving me freedom in exploring these concepts but guiding me, at the same time, in
order to better define the objectives of the thesis. I am really grateful for her incredible help, I
will always remember her advices and hope to have the possibility to work with her again in
the future.
I am very thankful to my external supervisor Prof. López-Lambas, Prof. Martín and Ing. Delso
and to all the centre of research TRANSyT for receiving me and helping me in this work, with
their reccomendations and great kindness. You all have enrich myself and my experience.
My friends. No word is appropriate to describe your importance. Thanks to the new ones met
in Madrid, for making me living a great and intense experience. Thanks to all the people met
during the studies in Rome, you made me feel at home from the first day, you are my second
family. Finally thanks to my old friends, my anchor and reference point, for your constant
support and laughs.
And thanks to my family, for giving me the chance to make this study career, supporting me in
all my needs and difficulties.