the impact of the built environment at trip origin...
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THE IMPACT OF THE BUILT ENVIRONMENT AT TRIP ORIGIN AND DESTINATION ON INDIVIDUAL MODE CHOICE: AN EMPIRICAL STUDY OF PORTLAND, OREGON
By
JIA FANG
A THESIS PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF URBAN AND REGIONAL PLANNING
UNIVERSITY OF FLORIDA
2017
© 2017 Jia Fang
To my family, teachers and friends
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ACKNOWLEDGMENTS
My special thanks go to my committee members Dr. Ruth Steiner, and Dr.
Sivaramakrishnan Srinivasan for their academic support and guidance throughout this
research. I sincerely thank Bud Reiff at Portland Metro Research Center for his patience
and time in providing me with detailed survey data of Portland, Oregon. I would also like
to thank the faculty and staff in the Department of Urban and Regional Planning for their
instruction and help. I am thankful to Changjie Chen, Guanqiong Guo, Wei Zhang,
Kaysie Salvatore, Huihui Nan and all my wonderful friends for their love and
encouragement. Last but not least, I would like to thank my mom, dad and little brother,
for their constant support in pursuing my degrees.
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TABLE OF CONTENTS page
ACKNOWLEDGMENTS .................................................................................................. 4
LIST OF TABLES ............................................................................................................ 7
LIST OF FIGURES .......................................................................................................... 8
LIST OF ABBREVIATIONS ............................................................................................. 9
ABSTRACT ................................................................................................................... 10
CHAPTER
1 INTRODUCTION .................................................................................................... 11
2 LITERATURE REVIEW .......................................................................................... 14
2.1 Relationship between the Built Environment and Travel Behavior .................... 14 2.2 Research on Mode Choice Behavior ................................................................ 15
2.3 Mode Choice Focusing on Non-Motorized Modes ............................................ 18 2.4 Research on Trip Chaining ............................................................................... 19 2.5 Roles of the Built Environment at Trip Origin and Destination .......................... 20
2.6 Summary .......................................................................................................... 22
3 METHODOLOGY ................................................................................................... 26
3.1 Study Area and Data Collection ........................................................................ 26 3.2 Definition of Independent Variables .................................................................. 28
3.2.1 Attributes of Alternative ........................................................................... 28 3.2.2 Socio-economic Characteristics .............................................................. 28
3.2.3 Trip Characteristics.................................................................................. 29 3.2.4 Built Environment Characteristics ............................................................ 29
3.2.4.1 Diversity ......................................................................................... 29 3.2.4.2 Density ........................................................................................... 31
3.2.4.3 Design ............................................................................................ 31 3.2.4.4 Accessibility/ Transportation supply ............................................... 33
3.3 Built Environment Factor Analysis .................................................................... 33
3.4 Descriptive Analyses ......................................................................................... 34 3.5 Correlation between Mode Choice and Independent Variables ........................ 39 3.6 Probabilistic Choice Theory .............................................................................. 42
4 MODEL RESULTS AND INTERPRETATION ......................................................... 43
4.1 Basic Model ...................................................................................................... 43 4.2 Partially Expanded Model with BE at Trip Origin .............................................. 45
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4.3 Fully Expanded Model with BE at Trip Destination ........................................... 46 4.4 Model with Trip Chaining as Interaction Term ................................................... 46
5 DISCUSSION ......................................................................................................... 51
6 CONCLUSION AND FUTURE RESEARCH ........................................................... 54
6.1 Conclusion ........................................................................................................ 54 6.2 Future Research ............................................................................................... 56
LIST OF REFERENCES ............................................................................................... 57
BIOGRAPHICAL SKETCH ............................................................................................ 61
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LIST OF TABLES
Table page 3-1 Factor Analysis Result ........................................................................................ 35
3-2 Descriptive Statistics for Independent Variables by Mode .................................. 37
3-3 Difference in Mode Shares by Trip Purpose and Location ................................. 39
3-4 Correlations between Mode Choice (coded 0-1) and Independent Variables ..... 41
4-1 Model Results ..................................................................................................... 48
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LIST OF FIGURES
Figure page 3-1 Map of study area and the trip origins by Household Location ........................... 27
3-2 Scree Plot ........................................................................................................... 35
3-3 Defining Process of Built Environment Factors................................................... 36
5-1 Mean Walking Distance by Trip Chaining and Mode .......................................... 53
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LIST OF ABBREVIATIONS
BE Built Environment
PNR Park and Ride
TOD Transit Oriented Development
VMT Vehicle Miles Traveled
WNR Walk and Ride
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Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Master of Urban and Regional Planning
THE IMPACT OF THE BUILT ENVIRONMENT AT TRIP ORIGIN AND DESTINATION ON INDIVIDUAL MODE CHOICE: AN EMPIRICAL STUDY OF PORTLAND, OREGON
By
Jia Fang
December 2017
Chair: Ruth L. Steiner Major: Urban and Regional Planning
Compact, walkable, mixed-use, and pedestrian-friendly urban environments help
decrease car-dependency and lead to green travel. This study investigates the impact
of built environment factors at trip origin and destination in shaping mode choice using
the 2011 Oregon Household Travel Survey in Portland, Oregon, which is important
because it supplements current research that has focused primarily on residential
neighborhood. In addition, the study measured the marginal contributions of not only
built environment factors, but their interaction with transit service level and trip chaining
behavior using the utility-based model.
The empirical modeling confirmed that the influence of the built environment at
trip destination on mode choice is significant and even stronger than that at trip origin.
Notable, urban compactness exerts far stronger impacts in promoting non-auto usage
than other built environment factors. Lastly, a high quality of the built environment at trip
destination will significantly improve the probability of walking among those who
participate in chain traveling.
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CHAPTER 1 INTRODUCTION
Since the 19th century, levels of auto ownership have risen significantly. It rose
fourfold from 1950 to 1990 all over the world (Lomborg, 2001). In 2016, approximately
17.5 million new cars were sold across the U.S, an unprecedented increase that has
continued to increase over the last seven years (Overly, 2017). Intensifying auto-
dependency has been blamed for climate change, greenhouse gas increases, traffic
congestion, conventional energy depletion and obesity. Many fields of study such as
transportation, public health, planning and environment health have made lots of efforts
to solve or alleviate these problems (Yang, Yan, Xiong, & Liu, 2013; Bhat, Sen, & Eluru,
2009; Cervero & Duncan, 2003).
The New Urbanism Movement, arising in the U.S. in the late 1980s, aims to solve
numerous urban issues associated with heavy auto-dependency by creating
environmentally friendly neighborhoods and encouraging non-auto use. Transit Oriented
Development (TOD), listed as one of the ten basic principles of New Urbanism
(Kelbaugh, 2002), is considered the most important principle of New Urbanism in that it
creates convenient traffic service and increases pedestrian-friendly communities.
Meanwhile, developing new strategies and policies on transportation and land use have
been proposed to discourage auto usage and reduce vehicle miles traveled (VMT). The
fundamental justification for these policies is the premise that the influence of the built
environment on traveler behavior is significant. Therefore, understanding how and to
what extent the built environment impacts traveler behavior will further support policy
makers with developmental suggestions. For example, if high levels of the built
environment lead to high ridership and more walking/biking trips, it is reasonable to
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assume that TOD residents would pay less transportation impact fees and fewer
parking spots will be built. In turn, these changes would reduce housing prices in those
areas.
In the past three decades, much attention has been paid to the relationship
between the built environment and travel behavior (Park, Choi, & Lee, 2015; Reilly &
Landis, 2002; McKibbin, 2011; Frank, Bradley, Kavage, Chapman, & Lawton, 2008). By
controlling for the characteristics of individuals and trips, a series of built environment
factors in three dimensions (diversity, density and design) have been examined by trip
purposes (e.g. commute, non-work trips, shopping trips, school trips and access trips).
Currently, most of research has focused on either residential neighborhoods or
specified places for activities. Very few have adequately specified the impact of the built
environment on whether they are measured at trip origin or destination. Therefore, as
trip chaining has become very important in people’s daily travel, research on how the
built environment interacted with trip chaining will help to better understand transport-
land use link.
Using the 2011 Oregon Household Travel Survey as well as detailed land-use
data of Portland, three objectives have been developed for this research:
1. Include an exhaustive set of mode choices in the analysis where access trips are
regarded as part of an entire trip;
2. Explore the different roles trip origin and destination play in mode choice
decision; in terms of significance of built environment factors;
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3. Examine the different impacts of the built environment at trip origin dependent on
transit service level and the different impacts of the built environment at trip
destination depending on whether it is a chain trip.
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CHAPTER 2 LITERATURE REVIEW
2.1 Relationship between the Built Environment and Travel Behavior
In the past, studies on travel behavior have mainly focused on the impact of
travel attributes and the socio-economic characteristics of the individual. Later, the link
between land use and transportation became widely recognized. As a result, many
studies began including land-use factors in travel behavior research (Aditjandra, pg 54-
65, 2013; Frank L. P., pg 44-52, 1994; Parks, pg 250-263, 2006). The LUTRAQ
Pedestrian Environment Factor (PEF) (1993) is a widely known survey that evaluates
neighborhood design on pedestrians evaluates non-motorized travel using a subjective
scale. Handy (1996) designated six neighborhoods in Austin, Texas as either traditional,
early-modern or late-modern to examine the impact of the urban form on pedestrian
choice. Cervero (1997) developed the concept of the 3Ds (density, diversity, and
design), which have had an increasing influence on many of the studies preceding it
However, the outcomes of many researches have little consistency. Cervero (1997)
confirmed the impact of density, land-use diversity and urban design on travel behavior,
“though the influence appears to be fairly marginal” (p. 199). Later, Boarnet and
Sarmiento (1996) found that the link between land-use and non-work-related travel
behavior was fairly weak and suggested all new research consider self-selection and
land-use variables be included as endogenous. In 2003, using the 1995 Portland
Metropolitan Activity Survey, Rajamani and Bhat et al. (2003) found that high levels of
mixed used development will promote walking for non-work travel, while Cervero and
Duncan (2003) contended that the impact of built environment factors are far less
impactful than travel and individual characteristics. Further they believe that stronger
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evidence is necessary in supporting land-use policy on travel behavior. One year later,
Lund and Cervero et al. (2004) found that, among design indicators, only those at
destination were significant. Recently, Ewing and Cervero (2010) discovered that
density is the weakest factor of the 3Ds, while Zhang (2012) noted that the
effectiveness of built environment factors differs across geographical locations and
sometimes they should work together to be effective. The reason for so much
inconsistency in the research could be because the studies focus on a different
geographical area. Different geographical areas can have different lifestyle and
demographic backgrounds, different accuracies in travel data and data collection (e.g.
Transportation Analysis Zone-based vs. half-buffer based, aggregate level vs.
disaggregate level), and different methods for measuring factors (e.g. entropy vs
dissimilarity of land uses).
Despite all the efforts that have been done by previous studies, it is still not clear
whether and to what extent the built environment impacts travel behavior.
2.2 Research on Mode Choice Behavior
A number of research approaches on land-use and travel behavior have been
used. These approaches include simulation studies, aggregate analyses, disaggregate
analyses, choice model and activity-based analyses (Handy S. L., 1996). Due to the
lack of high-quality travel data, the former two approaches were widely used in earlier
research (Frank L. P., pg 44-52, 1994; Lund, n.p., 2004; Friedman, pg 63-70, 1994;
Cervero R. G., pg 210-225, 1995).
Most recent studies on transport and land use link use disaggregate analyses
and choice model (Park, Choi, & Lee, 2015; Reilly & Landis, 2002; Zhang, Hong, Nasri,
& Shen, 2012; Lund, Cervero, & Willson, 2004), while other studies adopted activity-
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based analysis (Frank, Bradley, Kavage, Chapman, & Lawton, 2008; Xu, 2014).
Compared to aggregate analyses, disaggregate analyses measure at the individual and
household level. Many regression models have been estimated with different dependent
variables, including mode share, trip length, trip frequency, vehicle trips (VT) and
vehicle miles traveled (VMT).
Disaggregate studies do not directly reflect theories of choice processes while
the travel choice model is based on an individual’s utility of a particular alternative and
has a stronger theoretical basis (Handy S. L., 1996). Some of the research uses binary
or multinomial logit techniques which uses travel behavior to predict trip purpose. Using
the 1996 Bay Area Travel Survey, Reilly and Landis (2002) built multiple models that
probed the impact of urban form on travel behavior based on different purposes (non-
work trips, shopping trips and rapid transit train access trips). The study provided a
comprehensive examination on the built environment at residential areas, but ignored
the built environment at trip destination; the impact of which has been confirmed by
other studies (Handy S. L., 1996; Lund, Cervero, & Willson, 2004). Jae-Su Lee (2014)
compared two geographic scales (TAZ based and ¼ mile buffer) for both commute and
none-commute trips. The results confirmed the effectiveness of the built environment
and shows that the buffer-based measures are more reasonable. Only focusing on
commute trips, Chatman (2003) examined the impact of diversity and density at the
workplace on mode choice and found that employment density at the workplace could
reduce driving significantly. The study controlled for socioeconomic characteristics, but
failed to exclude the impact of travel characteristics and the built environment at trip
origin.
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Mode choice models were used to compare the different impact of the built
environment on travel behavior by neighborhood characteristics. Cervero and Radisch
(1995) compared travel behavior between two different neighborhoods (neo-traditional
and conventional suburban communities) and concluded that mode choice is
significantly affected by type of neighborhood. However, none of the mode-specific
indicators were included in the model and the only related indicator was a 0-1 dummy
variable; which was used to distinguish between the two neighborhoods. Hence, the
study failed to explain what aspects of urban form or attributes of the modes attributed
to the difference. Using two surveys from Boston and one from Hong Kong, Zhang
(2004) examined the impact of urban form in two geographically different areas. The
results indicated that the impact of land-use on driving was comparable to driving cost,
but the effectiveness of land-use strategies was place-based. Land-use data in the
study was collected based at the TAZ level, which was considered to be not accurate
enough by some studies (Reilly & Landis, 2002; Lee, Jin, & Lee, 2014; Park, Choi, &
Lee, 2015).
Lund and Cervero et al. (2004), focused on travel characteristics of TOD, their
study detailed a comprehensive analysis of the influence of TOD design on transit
ridership by trip purpose (commute, non-work and access trips) and trip data collected
at multiple levels. The study collected detailed neighborhood data of TOD sites in
California and surveyed TOD residents living within a half mile of a transit station. The
study found that it is necessary to discourage auto use by building streetscapes,
improving upon design and by increasing mixed-use development in TODs. However,
the impact of the urban environment might present as weak for TOD residents
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compared to non-TOD. Further, the shorter the travel distance to a station, an
individual will obtain fewer benefits of the well-designed environment. Therefore, it will
be helpful if the study could find if impacts of the built environment are felt based on
different transit service levels (for example, distance to station).
2.3 Mode Choice Focusing on Non-Motorized Modes
Before 2000, most research was motivated by promoting transit ridership and
decreasing auto-dependency. However, only a handful of studies included walking and
biking as an alternative mode (Loutzenheiser, 1997; Rajamani, Bhat, Handy, Knaap, &
Song, 2003). In response to the obesity crisis in America, increasing walking and biking
as forms of physical activity (PA) have become increasingly popular as of the early
2000s. Using household activity data from the Bay Area, Cervero and Duncan (2003)
examined the relationship between the built environment and non-motorized travel.
Applying the “3D” principle, they developed two separate binary choice models for
walking and biking trips. The study revealed that the impact of the built environment on
foot and bicycle travel was modest and sometimes even insignificant. They suggested
including micro-level variables, such as street furniture, should be considered in future
research.
To promote walking-related activities, some mode choice research focuses on
the transit users’ access to different types of model. From an environmental
perspective, promoting walking to transit would reduce air pollution. Additionally, transit
travel has little impact if most people drive to stations (Cervero, 2001). Using the 1992
Bay Area Rapid Transit (BART) Rider Survey Data, Loutzenheiser (1997) built three
walk-based binary logit models and found that the built environment is secondary to
individual characteristics. The study provided a comprehensive analysis of station area
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characteristics, but failed to control for the built environment at trip origins. Lund and
Cervero et al. (2004)examined factors affecting non-motorized access to rail stations.
The model was not convincing enough to explain for the variation in access to mode
choice and it revealed that only bright lights were significant. Sungjin Park (2015)
measured built environment factors on micro-level walkability for access trips.
Socioeconomic status, trip origins and walking routes of 249 transit users were collected
for the study. The results suggested that micro-level walkability could significantly
promote walking travel. Since the survey was conducted in one station, the impact of
the built environment at the station was not able to be examined. In spite of the benefits
of walking as a primary model choice, research on walking behavior and access mode
choice is still quite rare.
In conclusion, most mode choice studies on the link between the built
environment and mode choice focused on either main trips or access trips. Lund and
Cervero et al. (2004) and Reilly and Landis (2002) might have conducted the only two
studies that examined both types of trips, but none of them analyzed these two types
simultaneously. Additionally, both of their access mode choice models relied on
indicators designed for analyzing main trips, which might reduce the accuracy of the
access model.
2.4 Research on Trip Chaining
In the past few decades, trip chaining has increased significantly. Studies show
that trips have increased mainly from work travel and, as a result, trip chaining has
become an important part of many commuters’ daily travel. According to the 2001
National Household Travel Survey, commuters who trip chained were more likely to use
private vehicles, while transit share for commuters who chain (3.6%) was lower than
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that for persons who made direct trips (6.3%) (McGuckin, Zmud, & Nakamoto, 2005).
As a result, many studies have estimated people’s travel behavior using “tours” instead
of single trips. Cynthia Chen (2008) studied the role of employment density on mode
choice by using tour as the analysis unit. Many studies have confirmed the importance
of trip chaining in mode choice decision. Henscher and Reyes (2000) developed a
series of discrete choice models to support the argument that increasing complexity of
trip chains reduce the utility of transit. Similarly, Lund and Cervero et al. (2004) found
that trip chaining had a significant impact on using mass transit by including trip
chaining behavior as a dummy variable. Meanwhile, they found that more mixed-use
environments seemed to promote walking, allowing transit riders to chain trip ends in a
more diverse setting. This indicated that those who chain are more likely to walk to
stations; as land-use at home end is more diverse. Chatman (2003) mentioned that high
diversity and density at the workplace will promote transit use. However, so far, trip
chaining is still rarely considered in studies that focus on the relationship between the
built environment and mode choice.
2.5 Roles of the Built Environment at Trip Origin and Destination
Empirical studies generally confirmed that the built environment characteristics of
both trip origins and destinations significantly affected the probability of travel mode
choice, given other variables in the choice model. Handy (1996) argued that
the environmental quality of both residential and trip destination will be important to
walking trips. Cervero et al. (1997) stated that built environmental characteristics of
origin and destination interchanges will influence travel demand, similar to price and
quality of competing modes. Evaluating the impact of land-use plans on VMT, Zhang
(2012) indicated that the model’s explanatory power could be increased if involving built
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environmental factors at destinations. However, much attention has been paid to
residential areas, while trip destination is overlooked. Only some research focusing on
specific trip purposes examine the location of trip destination in detail. Ewing et al.
(2004) examined the full range of built environment factors at school locations as
affecting mode choice for school trips in Gainesville, FL. However, the outcomes in the
analysis were only partly consistent with earlier research on school trips; requiring
further research. Using the 1995 Nationwide Personal Transportation Survey, Chatman
(2003) studied the impact of the built environment at the workplace on commuters’
travel behavior. The study found that employment density and land-use diversity at the
workplace have a significant impact on personal and commercial VMT and automobile
commuting. He explained that shops and services near the workplace would promote
non-auto commuting is because workers might “carry out activities on foot before,
during, and after the workday” (p. 193). Therefore, the study results confirmed the
importance of trip destination on travel behavior.
Several studies have examined both trip origin and destination together (Lee, Jin,
& Lee, 2014; Ewing & Cervero, 2010; Lund, Cervero, & Willson, 2004; Hess, 2001),
Some concluded that the impact of the built environment at origin and destination is
different. Handy (1996) found that travel distance, combined with the quality of the
walking environment at trip destination, outweighed the quality of the built
environment surrounding residential areas in travelers’ choice to walk. The binomial
model of drive-alone commuting developed by Cervero (2002) indicated that each built
environment factor at trip destination was more significant than that at trip origin.
Furthermore, after examining neighborhood design factors at both trip origin and
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destination, Lund and Cervero et al. (2004) found that for station area residents, the
only significant neighborhood-design variable was the level of street connectivity at the
destination. Similarly, Zhang M (2004) examined four built environment factors (job
densities, population densities, land-use balance, and cul-de-sac percentage) at both
origin and destination for main trips in Boston, Massachusetts. Surprisingly, the results
showed that all four indicators are significant at trip destination, while none are
significant at trip origin. Based on these findings, we might surmise that “origins might
play a different role from the destinations” (Chen, Gong, & Paaswell, 2008, p. 288).
However, all the above studies failed to provide convincing statistical evidence. Even
though a study (Cervero & Duncan, 2003) found that built environments have stronger
impacts in residential neighborhood than at destination in terms of the statistical fits, the
result was inconsistent with other studies’ findings.
Despite many empirical works, most previous studies focused on residential
neighborhoods. Only a few examined both sides of a trip simultaneously and none have
adequately specified the roles of built environment factors at trip origin and destination
in shaping mode choice.
2.6 Summary
By including a full set of modes (drive, walk and ride, park and ride, walk and
bike), this study will explore the different roles that the built environment at trip origin
and destination play during an individual’s travel decision making process. While also
studying how the built environment interacts with trip chaining and transit service level.
The three objectives are explained as followed:
1. Include an exhaustive set of mode choices in the analysis where access
trips are regarded as part of an entire trip. Generally, separate studies are conducted for
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main trips and access trips to transit on mode choice. The inclusion of transit as an
alternative instead of park and ride (PNR) and walk and ride (WNR) is inaccurate and
inappropriate in many choice situations. For example, travel time is different for a trip by
WNR and PNR; the impact of vehicle ownership on PNR is stronger compared to WNR.
In addition, the built environment exerts different influences on WNR and PNR. For
example, it has been confirmed by many studies that low levels of the built environment
would discourage public transit usage relative to driving. However, considering access
to mode choice adds an additional level of complexity to studying trip generation. When
the level of the built environment at trip origin decreases, WNR users will be diverted to
drive or PNR; meaning that there is a chance that transit ridership will not decrease.
Therefore, the inclusion of access modes helps to truly understand the impacts of the
built environment on individual mode choice.
2. Explore the different roles trip origin and destination play in mode choice
decision; in terms of significance of built environment factors. As Cynthia Chen (2008)
and Zhang (2004) contended, the built environment at trip origin might play a different
role from that at trip destination for mode choice. For example, in the case of mode
choice between WNR and PNR, it is reasonable to surmise that the quality of the built
environment at trip origin has stronger impacts compared to trip destination. This is why
most studies on transit access trips (Park, Choi, & Lee, 2015; Park, Kang, & Choi, 2014;
Reilly & Landis, 2002) have focused on the path from the household to transit stations.
Furthermore, research (Cervero, 2002; Chen, Gong, & Paaswell, 2008; Lund, Cervero,
& Willson, 2004; Handy S. L., 1996; Zhang, Hong, Nasri, & Shen, 2012) found that
significance or quality of the built environment at trip destination outweighs that at trip
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origin in terms of the magnitude and significance of variables. However, the study by
Cervero (2003) hints a reversed result based on the statistical fits of the model. Using
the travel survey in Portland, this study will examine the difference between the roles
that trip origin and destination play in mode choice.
3. Examine the different impacts of the built environment on trip origin
dependent on transit service level and the different impacts of the built environment on
trip destination given its involvement in trip chaining. In TOD, high quality transit
systems are surrounded by high-levels of built environment. Examples include
continuous sidewalks, attractive landscaping and densely distributed street canopy.
According to research, it is believed that individuals care less about the built
environment when they live close to a transit station. This study tried to answer whether
the proximity to a transit station will reduce the degree of significance of the built
environment on TOD residents’ travel behavior by examining the interaction of service
level and the built environment at trip origin.
Hensher and Reyes (2000) contended that trip chaining is a barrier to public
transit. However, some studies on the built environment stated that high-levels of the
built environment would discourage auto usage by offering convenience for trip
chaining. Therefore, it seems that the research on trip chaining is inconsistent. This
study will test the varying degree of significance of BE attributes at trip destination
depending on whether it is a chain trip.
Overall, three sets of analysis will be performed. The first analysis is conducted
using a simple correlation test to determine the relationship between transit codes (0-1)
and built environment variables at both trip ends in terms of sign and magnitudes of the
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coefficients. The second analysis uses multinomial logit models to test the role that trip
origin and destination play in mode choice decision in terms of significance and
magnitude of the built environment variables. The third analysis uses multinomial logit
models to test the different impacts of the built environment with respect to trip-chaining
behavior. Discovering the different roles that trip origin and destination play in
determining the link between the built environment and mode choice will hopefully
increase the efforts made towards promoting TOD at both ends of the trip as well as
provide advice to transportation land-use policies and urban planning for specific modes
or areas.
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CHAPTER 3 METHODOLOGY
3.1 Study Area and Data Collection
The study is set in Portland, Oregon, which is served by a comprehensive public
transportation system. Because Portland has a strong tradition of promoting transit-
oriented development, with lots of successful TOD projects like Acadia Garden and
Merrick Apartments, clarifying the marginal roles of urban form factors in shaping mode
choice in such settings takes on particular importance. Currently, the transit system has
a high rate of use (20.2% for work trips in 2011). 610 buses operate on a network of 80
routes every day. Twelve of the routes are express bus routes with a 15-minutes-or-less
wait time on weekdays. Portland’s light rail system is comprised of five lines and the
two-line streetcar system serves the downtown and its surrounding area.
Three data sources are used for the study. The first is the 2011 Oregon Travel
and Activity Survey which is compiled by the Oregon Department of Transportation
(ODOT). The second data source is the Oregon Travel and Activity Survey (OTAS)
which is the first in-depth study of household travel behavior in Oregon in more than ten
years. From April 2009 through November 2011, approximately 18,000 households
were surveyed to identify where and how they traveled on a specific, designated travel
day (24 hours). The study collected geographic data of the greater Portland area from
two sources, 2010 US Census TIGER files and the CivicApps website. The CivicApps
website offers all aspects of geographic information of the area, including road network,
street facilities and transit routes.
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Most of the geographic data that the study collected is across the City of
Portland. As a result, 2,974 trips, with both trip origin and destination were used for this
study. The study defines areas within a quarter mile radius of frequent bus or light
rail/streetcar stations as high-level transit service areas. Figure 3-1 shows a map of
Portland with the origin points of 2,974 trips and high-level transit service areas in pink.
The yellow points are those within the buffer of high-level transit service, while the green
points are out of the buffer area.
Figure 3-1. Map of study area and the trip origins by Household Location
The travel mode choice set for each trip represents model availability at the
household level: 1) Auto drive is considered as being available if the individual has a
vehicle in the household and is a licensed driver; 2) Walk-and-ride (WNR) is designated
as being available to trips of which total access and egress walk time is less than thirty
minutes and maximum transfer time is less than two minutes (this criterion is provided
by Portland Metro); 3) Park-and-ride (PNR) is considered as being available if the
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individual has a vehicle in the household and total access and egress walk time of the
trip is less than thirty minutes and maximum transfer time is less than two minutes; 4)
Walk is designated as being available to individuals whose trip distance is less than the
maximum distance walked by an individual in the sample (3.66 miles); 5) Bike is
designated as being available if the individual has a bike in the household.
Of the 2,974 trips, 74.8% of them fell within the walking mode, 95.45% fell within
the driving mode, 80.46% fell within the PNR mode, 85.29% fell within the WNR mode
and 74.98% fell within the biking mode.
3.2 Definition of Independent Variables
This study includes four sets of variables: attributes of alternative, socio-
economic characteristics, trip characteristics, and built environment measures.
3.2.1 Attributes of Alternative
Attributes of alternative represent level-of-service variables of competing modes.
The in-vehicle travel time and out-of-vehicle travel time variables were combined into a
single time variable. The average walking speed of an individual (3.1 miles per hour)
was used to calculate walking time while the average biking speed of an individual (15.5
miles per hour) was used to calculate biking time. Separate time coefficients were
estimated for competing modes to accommodate for the differential time sensitivities
based on travel mode. Travel cost was also included.
3.2.2 Socio-economic Characteristics
The household socio-economic attributes considered in the study include
household size, income, vehicles per person and drivers licenses per person. The
individual socio-demographic characteristics used include sex, age, educational
attainment, ethnicity, student status and employment status.
29
3.2.3 Trip Characteristics
Trip characteristics represent variables exclusively related to the attributes of
trips. In this study, two dummy variables are used to represent trip characteristics: trip
purpose (work or non-work trip) and trip chaining behavior (chain trip or single
destination trip).
3.2.4 Built Environment Characteristics
Built environment characteristics measure incorporated in the study are divided
into four categories: diversity, density, design, and accessibility. With the use of census
block-based land-use data and GIS techniques, the study measures the built
environment within a quarter of a mile radius of trip origin and destination. The four built
environment categories are explained below in detail:
3.2.4.1 Diversity
Diversity focuses on the degree of variability for land-use and employment.
Diversity promotes accessibility to a variety of activities. Three diversity variables are
considered in the analysis:
Job mix measures the degree of job mixing at the buffer area. Using 2011
Census data, jobs are classified into five types to measure the variability. The value
ranges from 0 (where one employment sector dominates) to 1 (where all jobs are evenly
distributed between the five sectors). The equation for job mixing can be found in
Equation 3-1.
𝐽𝑜𝑏 𝑀𝑖𝑥 = 1 − {(| [management]
[sum]−
1
5| + |
[service]
[sum]−
1
5| + |
[sales]
[sum]−
1
5| +
| [production]
[sum]−
1
5| + |
[natural]
[sum]−
1
5|) /
8
5} (3-1)
30
Mixed-use level indicates extent to which the surrounding environment of the
place develops buildings with a mixed use of land. The greater amount of mixed-use,
the greater opportunity of physical activity taking place around residential areas. A study
conducted by Chatman (2003) indicates that land use diversity at the workplace will
promote non-auto commuting and the potential for trip chaining by walking/biking before
and after work. Several measures have been used by to capture the effect of mixed-
use, such as the dissimilarity index.
This study employs the entropy index to measure the degree of land-use
heterogeneity. Seven land-use types of original zoning data are reclassified into five
simple types: commercial, industrial, mixed-use, multi-family and single-family, parks
and open space. Entropy is computed by using the following equation:
𝐸𝑛𝑡𝑟𝑜𝑝𝑦 = −(∑ 𝑃𝑗 ∗ 𝐼𝑛𝑃𝑗𝑛𝑗=0 )/𝐼𝑛(𝑛) (3-2)
where Pj is the proportion of land-use in the j-th land-use category and j is the number
of different land-use type classes in the area. The entropy measure ranges from 0
(where one land-use type is dominant) to 1 (where land-uses are equally mixed).
The jobs-resident balance Index is an index that measures the balance between
employment and resident population. The value ranges from 0 to 1. A value of 1 means
that the area has the same ratio of jobs to residents, while a value of 0 indicates that
either jobs or residents predominate. The equation for the jobs-resident balance index is
found below as Equation 3-3.
𝐽𝑂𝐵𝑃𝑂𝑃 = 1 − (|employment−a∗population
employment+a∗population|) (3-3)
where a is the regional ratio of employment to residents.
31
3.2.4.2 Density
Density theorizes that the average trip distance is reduced in relation to major
transport nodes, amenities and jobs. An increased density increases the probability of
walking, biking and using public transit (Cervero & Kockelman, 1997). This study
focuses on two densities: population density and gross density/activity.
Population density is measured as the number of residents per acre within a
quarter mile radius of the destination.
Gross density/activity measures the overall density of the buffer area in terms of
people either living or working within the destination area (Ewing, Schroeer, & Greene,
2004; Cervero, 2002).Gross density can be measured using the following equation:
𝐺𝑟𝑜𝑠𝑠 𝑑𝑒𝑛𝑠𝑖𝑡𝑦 = (𝐽𝑜𝑏𝑠 + 𝑝𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛)/𝑎𝑟𝑒𝑎 (3-4)
3.2.4.3 Design
Design reflects the aesthetics of a neighborhood and the physical configurations
of street networks; which emphasize an individual’s walking experience. This set of
variables includes a connectivity index and the percentage of cul-de-sac streets in the
neighborhood. Multiple attributes are used in calculating a design index of which are
mentioned below.
Sidewalk coverage rate is one of the most common elements used to evaluate
walkability level. Streets without sidewalks decrease the probability of walking because
it forces pedestrians to share the road with fast-moving vehicles. A majority of studies
focus on the average width of sidewalks, the presence of sidewalks, and the continuity
of sidewalks (Park S. , 2008; Boarnet, Anderson, Day, McMillan, & Alfonzo, 2003).
Cervero (2002) found that the sidewalk ratio at both trip ends is significant in examining
built environment factors. This study uses sidewalk coverage rate to capture this
32
element. Sidewalk coverage rate is measured as the total length of sidewalks with a
quarter mile radius of the site divided by the total length of streets in the same area.
Street lighting is a component of street facilities. More street lighting signifies
greater luminosity at night which increases pedestrian and bicyclist sense of security.
Therefore, street lighting is supposed to have a positive impact on walkability level. This
study measures the street lighting coverage rate using the number of street lightings per
sidewalk foot.
Street trees are a component of street facilities as well. A lot of research has
shown the importance of street trees for pedestrians and bicyclists. From the
perspective of urban design, Jacobs (1992) emphasized its two advantages: shading
from sunlight and protected buffer for pedestrians/bicycles. Therefore, street trees
enhance the sense of comfort and safety, resulting in an increased walkability level.
Similarly, this study measures street tree coverage rate using the number of street trees
per sidewalk foot.
Connectivity is measured by two methods, street connectivity and intersection
connectivity. Street connectivity is measured as the length of all streets divided by the
number of intersections. Intersection connectivity is measured as the number of
intersections divided by total number of intersections and cul-de-sac within a quarter
mile radius of the destination. Higher connectivity provides travelers with various
potential routes.
Street density is quantified by the total street length divided by total area of land
within a quarter mile radius of the destination. It measures the degree of network
spreading.
33
Small blocks contribute to relatively direct routes, while grid networks with large
blocks and curvilinear streets increase walking distance by requiring circuitous routes.
Block size is measured as both the average and standard deviation block size within a
quarter mile radius of the destination. Using the mean may seem intuitive, but it does
not capture variance in block size; which is important to walkability.
Bicycle trail density is measured by taking the total bicycle trail length and
dividing it by the total area of land within the buffer area. It measures the degree of the
bicycle network spreading.
3.2.4.4 Accessibility/ Transportation supply
Transit service level represents the quality of transit service of the buffer area.
The transit service level variable measures frequency of bus stops and street-car/light
rail stations within the buffer area.
Route density measures the number of transit routes running through the buffer
area. The more routes, the more destinations the residents can reach by transit.
More recently, it has been observed that commercial land-use (including
restaurants, stores and entertainment facilities) allows for more frequent physical
activities. The accessibility to commercial land-uses and activities variable measures
activity by the percentage of sales jobs within the buffer area.
3.3 Built Environment Factor Analysis
In total, seventeen built environment variables were collected at both trip origin
and destination. Some of the variables are related, which could pose problems during
the model building process. Therefore, factor analysis was used to help extract the
underlying factors that captured common attributes of those different measurements of
built environment. According to the correlation test, ten variables had a correlation score
34
greater than 0.3 but less than 0.9. Because those variables had high correlation to
each other, they were combined. Seven variables were left independent of the other
variables because they did not have a strong relationship with any of the other
variables. Principal component method of factor extraction was adopted to reduce the
number of variables. Based on Kaiser’s criterion and the scree plot (Figure 3-2), two
factors with high factor loadings (>1) were extracted. This accounted for 66% of the
variance in the ten related variables. The rotated component matrix (oblique rotation)
was then used to improve the interpretation of the two variables extracted by the Kaiser
criterion scree plot. As shown in Table 3-1, the variables that load highly on factor 1 are
standard deviation for block size (-0.84), average block size (-0.81), street connectivity
(0.74), street density (0.69), bike trail density(0.68), intersection density(0.52), and
sidewalk coverage ratio(0.43), therefore the first component presents the importance of
network connectivity. The variables that load highly on factor 2 are gross activity (0.93),
route density (0.90), sidewalk coverage ratio (0.56), population density (0.55), therefore
the second component presents the importance of intensity. The two extracted factor
scores entered in the study as built environment variables, varying among individuals.
Figure 3-3 shows the workflow behind defining the built environment variables.
After running the factor analysis, nine built environment variables remained. Besides the
transit service level variable, all variables were used for both trip origin and destination.
In conclusion, 16 built environment variables were entered into the model analysis to
examine the roles of trip origin and destination.
3.4 Descriptive Analyses
Error! Reference source not found. summarizes the descriptive statistics for
Independent variables by
35
Figure 3-2. Scree Plot
Table 3-2. Factor Analysis Result
Connectivity Factor Intensity Factor
STD. Block Size -0.84 Average Block Size -0.81 Street Connectivity 0.74 Bike Trail Density 0.68 Street Density 0.69 0.37
Intersection density 0.52 0.39
Sidewalk Coverage Rate 0.43 0.56
Pop Density
0.55
Activity density
0.93
Route density
0.9
Summary Statistics
SS loadings 3.56 3.02
Proportion Var 0.36 0.3
Cumulative Var 0.36 0.66
36
Figure 3-3. Defining Process of Built Environment Factors
mode. The final sample size used for the analysis was 2,974 individual home-started
trips; out of which 1,718 began within high transit service level areas. The overall mode
shares are as follows: drive (63.3%), PNR (1.5%), WNR (9.8%), walk (16.0%) and
bicycle (9.4%).
Table 3-3 shows the difference in mode share by trip purpose, transit service
level and trip chaining behavior. In terms of transit service level, trips started from areas
with high transit service had a higher percentage of walking (18.3%), biking (11.1%),
and WNR (12.4%). Additionally, trips started from areas with high transit service had
lower percentages of driving (56.3%) and PNR (1%). It could be explained that areas
with high levels of transit service are usually accompanied by high walkability scores;
something that is consistent with New Urbanist ideals. High ridership rates in transit
villages are partly due to self-selection – people purposely live near transit stations
purposely for economizing on public transit. In this study, 784 households provided a
reason for choosing their current housing location. Of the 784 households, 198
households (approximately 25.2%) moved for access to transit. When comparing work
trips to non-work trips, a higher percentage of work trips were made by biking or using
multi-use transit (WNR and PNR); while a lower percentage of work trips were made by
driving or walking. In this study, the average distance for a work trip was 3.34 mile and
37
Table 3-2. Descriptive Statistics for Independent Variables by Mode
Bike Drive PNR Walk WNR
Independent Variable
Mean SD Mean SD Mean SD Mean SD Mean SD
Travel Time(minutes)
10.02 7.10 8.09 5.60 37.30 11.30 13.70 9.58 33.5
1 12.2
8 Travel Cost (US dollar)
0.00 0.00 0.72 0.53 1.98 0.35 0.00 0.00 1.85 0.46
Travel Distance(mile)
2.02 1.49 2.51 2.02 5.86 1.93 0.41 0.36 3.53 2.55
Household Size 2.67 1.10 2.64 1.23 2.39 1.02 2.64 1.20 2.22 1.14
Gender(Male=1) 0.61 0.49 0.45 0.50 0.39 0.49 0.44 0.50 0.45 0.50
Education Leve l(ordinal data from 1 to 6)
4.81 1.48 4.63 1.42 4.17 1.50 4.42 1.73 4.27 1.75
Bike per Person 1.24 0.71 - - - - - - - -
Vehicle per Person
0.56 0.35 0.85 0.45 0.91 0.43 0.59 0.38 0.52 0.44
Household Income (ordinal data from 1 to 8)
5.34 1.81 5.44 1.76 5.36 1.54 5.14 1.93 4.50 1.97
Age (ordinal data from 1 to 8)
5.18 1.57 6.08 1.35 5.33 1.45 5.29 1.98 5.41 1.93
Driver's License per Person
0.79 0.26 0.82 0.23 0.84 0.22 0.76 0.27 0.72 0.35
Employment (full job=1)
0.86 0.35 0.71 0.45 0.89 0.32 0.64 0.48 0.69 0.47
Is a chain trip (True=1)
0.39 0.49 0.46 0.50 0.50 0.51 0.23 0.42 0.38 0.49
Is work trip(True=1)
0.49 0.50 0.28 0.45 0.80 0.40 0.14 0.34 0.52 0.50
Mix-use Level at Oa
0.28 0.19 0.28 0.18 0.24 0.17 0.29 0.19 0.30 0.19
Job mix at Oa 0.41 0.16 0.40 0.16 0.37 0.16 0.44 0.14 0.42 0.14
Jobs-resident Balance at Oa
0.47 0.28 0.39 0.27 0.35 0.30 0.47 0.27 0.47 0.28
Sale Job Percent at Oa
0.20 0.20 0.19 0.19 0.16 0.16 0.21 0.18 0.20 0.19
Tree coverage at Oa
0.10 0.04 0.09 0.04 0.07 0.04 0.10 0.04 0.09 0.04
aO presents Origin bD presents Destination
38
Table 3-2. Continued
Bike Drive PNR Walk WNR
Independent Variable
Mean SD Mean SD Mean SD Mean SD Mean SD
Lighting coverage at Oa
0.01 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.01 0.00
Connectivity at Oa
0.19 0.60 -0.14 0.84 -0.69 0.97 0.23 0.63 0.08 0.85
Intensity at Oa -0.38 0.40 -0.51 0.33 -0.64 0.30 -
0.27 0.55 -0.25 0.61
Mix-use Level at Db
0.34 0.18 0.36 0.18 0.25 0.14 0.31 0.18 0.30 0.18
Job Mix at Db 0.48 0.15 0.46 0.15 0.46 0.11 0.47 0.14 0.48 0.13
Jobs-resident Balance at Db
0.44 0.29 0.51 0.30 0.21 0.22 0.56 0.27 0.37 0.30
Sale Job Percent at Db
0.20 0.15 0.21 0.16 0.15 0.07 0.20 0.16 0.18 0.12
Tree coverage at Db
0.02 0.01 0.02 0.01 0.02 0.01 0.03 0.01 0.02 0.01
Lighting coverage at Db
0.01 0.00 0.01 0.01 0.01 0.00 0.01 0.00 0.01 0.01
Connectivity at Db
0.27 0.79 -0.03 1.06 0.49 0.65 0.20 0.99 0.35 0.76
Intensity at Db 0.69 1.40 0.19 1.04 2.51 1.33 0.26 0.91 1.55 1.68
aO presents Origin bD presents Destination
1.80 mile for non-work trips. Because distance is highly influential on walking, it is
reasonable to assume that less people are likely to commute by foot. Further, those
who travel during peak hours are less likely to drive. Additionally, non-work trips
traveling to multiple locations with multiple people are more like to drive. In terms of trip
chaining behavior, chain trips have a higher percentage of auto usage (70.9%), but
lower percentage of walking (9.1%); which could be explained by the fact that chain
trips have longer distances than singular trips.
39
3.5 Correlation between Mode Choice and Independent Variables
Table 3-4 summarizes the correlations between mode choice (coded 0-1) and the
independent variables. Without controlling for any other factors, correlation
Table 3-3. Difference in Mode Shares by Trip Purpose and Location
Mode Overall Percent
high service level
low service level
non-work trip
Work trip
Non-chain trip
Chain trip
bike 9.40% 11.40% 6.70% 6.90% 14.90% 9.60% 9.10%
drive 63.30% 56.30% 72.90% 65.80% 57.80% 58.00% 70.90%
PNR 1.50% 1.00% 2.20% 0.40% 4.00% 1.30% 1.90%
walk 16.00% 18.30% 12.00% 20.00% 7.00% 20.80% 9.10%
WNR 9.80% 12.40% 6.20% 6.90% 16.20% 10.30% 9.10%
presents the relative impact of the variable on whether a trip was made by the
corresponding mode. The sign of the correlation coefficients reflects the direction of the
impact – positive values denote that the variable increases the probability of choosing
that mode while negative values indicate the opposite. Absolute correlation values over
0.1 are regarded as moderate to strong relation.
For non-motorized modes (bike and walk) versus automobile, all built
environment variables at trip origin and destination have positive moderate to strong
values. This means that high levels of urban environment at both trip ends will promote
non-motorized usage.
For WNR versus drive and PNR, all the correlations of the built environment
variables at trip origin are positive. This means that greater built environment variables
at trip origin promote WNR. Notably, intensity and connectivity factors on WNR versus
PNR have a relatively large correlation; indicating that at the residential neighborhood
level, intensity and connectivity might significantly increase the propensity to use WNR
relative to PNR. At trip destination, both connectivity and intensity are positively
40
correlated with transit usage (WNR and PNR). Because outbound trips are normally
made by non-motorized vehicles, higher connectivity and intensity are expected and
41
Table 3-4. Correlations between Mode Choice (coded 0-1) and Independent Variables
Bike_Drive Walk_Drive WNR_Drive PNR_Drive WNR_PNR
Travel Cost -0.57 -0.68 0.51 0.24 -0.08 Travel Time 0.10 0.27 0.56 0.26 -0.13 Distance -0.05 -0.57 0.18 0.20 -0.39 Household Size 0.00 -0.03 -0.16 -0.02 -0.13 Household Income -0.01 -0.08 -0.17 -0.01 -0.16 Gender(Male=1) 0.11 0.01 -0.01 -0.03 0.07 Education Level 0.08 0.03 -0.02 -0.05 0.08 Driver's License per Person -0.02 -0.05 -0.05 0.01 -0.07
Vehicle per Person -0.22 -0.23 -0.27 0.02 -0.29 Employment (full job=1) 0.10 -0.06 -0.02 0.06 -0.15
Age -0.16 -0.10 -0.08 -0.07 0.07 Is a chain trip (True=1) 0.04 0.18 0.04 -0.03 0.11
Is work trip(True=1) 0.17 -0.12 0.19 0.17 -0.16 Transit service (high level=1) 0.13 0.14 0.15 -0.04 0.27
Mix-use Level at Oa 0.00 0.03 0.04 -0.03 0.10 Job Mix at Oa 0.02 0.09 0.02 -0.02 0.07 Jobs-resident Balance at Oa 0.10 0.14 0.11 -0.02 0.14
Sale Job Percent at Oa 0.02 0.08 0.03 -0.01 0.05
Tree coverage at Oa 0.14 0.12 0.01 -0.07 0.22 Lighting coverage at Oa 0.09 0.14 0.12 0.01 0.10
Connectivity at Oa 0.13 0.19 0.11 -0.09 0.30 Intensity at Oa 0.13 0.22 0.19 -0.07 0.31 Mix-use Level at Db -0.04 -0.10 -0.13 -0.10 0.09 Job mix at Db 0.04 0.01 0.03 0.00 0.03 Jobs-resident Balance at Db -0.09 0.06 -0.17 -0.14 0.17
Sale Job Percent at Db 0.00 -0.02 -0.04 -0.04 0.06
Tree coverage at Db 0.06 0.22 0.03 0.01 0.00 Lighting coverage at Db 0.04 -0.01 0.06 0.02 0.02
Connectivity at Db 0.12 0.12 0.18 0.11 -0.05 Intensity at Db 0.15 0.08 0.29 0.22 -0.21 aO presents Origin bD presents Destination
42
conducive to PNR and WNR, compared to driving. However, variables on the diversity
dimension (jobs-resident balance and mixed-use level, sale job percent) are counter-
intuitively associated with transit use (PNR and WNR), which might lead to inconsistent
result with previous studies.
3.6 Probabilistic Choice Theory
This study develops logistic choice models to process and predict probabilistic
mode choice using the deterministic utility function; which is based on the assumption
that the individual is assumed to choose an alternative if the utility is greater than that of
any other alternative. The utility function is composed of two parts. One component is
the deterministic portion of the utility function (Vit) and the other is the difference
between the error and the portion of the unobserved utility used (εit).
3-5 Uit = Vit + εit
In this study, the deterministic portion of the utility function is composed of five
parts: exclusively related to the attributes of alternatives V (St), exclusively related to the
individual’s socioeconomic and demographic attributes V (Xi), exclusively related to the
attributes of trips V (Tt ), exclusively related to the built environment attributes of trip
ends V(BEt) and the interactions between the trip attributes and the BE attributes
V(Tt,BEt). The formula can be found as Equation 4-2 below.
3-6 V t,I = V(St) + V(Xi) + V(Tt) + V(BEt) + V(Tt,BEt)
According to the multinomial logit model (MNL), the choice probability of each
alternative has a mathematical function of the systematic portion of the utility of all the
alternatives. The choice probability of choosing alternative i could be expressed as:
3-7 Pr (𝑖) =exp (𝑉𝑖)
∑ exp (𝑉𝑖)𝐽𝑗=1
43
CHAPTER 4 MODEL RESULTS AND INTERPRETATION
Four mode choice models were tested in this study: 1) A basic model with
attributes of alternatives, socio-economic characteristics and trip attributes; 2) A partially
expanded model, which adds built environmental variables at trip origin to the basic
model; 3) A fully expanded model, which adds environmental variables at trip
destination to the partially expanded model; 4) A model with interactions between the
built environment variables at trip destination and trip chaining variables.
The coefficients estimated in the MNL model represent the influence of
exogenous variables on the latent utilities of the alternatives. The drive alternative is
used as the base for this study and aims to find the impact of the built environment on
prompting non-auto usage. Table 4-1 shows the model results with all the statistically
significant variables. The sign of the coefficients reflects the direction in which each
variable impacts the corresponding mode over automobile use – positive values denote
that a variable increases the probability of choosing that mode versus car while
negatives indicate the opposite.
4.1 Basic Model
The basic model reveals that longer travel time will significantly decrease a
travelers’ utility of driving, biking, walking, PNR and WNR at the 0.001 significance level.
Several socio-economic variables in the basic model were significant predictors.
It is reasonable to believe that vehicle ownership will encourage auto and PNR use
relative to walking, biking and WNR. Household size had a significantly negative impact
on non-auto usage (walk, WNR and bike), which is expected and consistent with
previous study (Rubin, Mulder, & Bertolini, 2014). Larger household sizes are more
44
likely to make multi-member trips; increasing the probability of trip chaining and requires
high flexibility in scheduling. Therefore, car travel by larger households requires higher
flexibility and lower marginal costs (Rubin, Mulder, & Bertolini, 2014). Higher educated
individuals are more likely to travel using non-motorized modes; which is consistent with
Loutzenheiser’s study (1997). Additionally, age is positively associated with auto usage
and males are more likely to bike over drive at the 0.001 significance level. For work
trips, individuals are more likely to commute by bike and transit (PNR and WNR) versus
driving. These results are consistent with the results from the mode share analysis in
the previous chapter. It is also expected that work trips will promote walking, after
controlling for the effects of travel time.
As mentioned in the second chapter, previous studies found that the complexity
of a trip will discourage transit use because it is more convenient and flexible to do
multiple errands by driving. Consistent with their findings, the model found that trip
chaining (as a dummy variable), has a negative impact on biking, walking and WNR
compared to drive. Consistent with previous studies (Chatman, 2003; Chatman, 2003;
Lund, Cervero, & Willson, 2004), this study indicates that people who chain are more
likely to drive, as car travel is associated with higher flexibility in scheduling.
The presence of frequent or high-capacity service within a quarter mile radius of
residential locations will promote WNR relative to driving. Combined with the absence of
the service variable for the PNR mode, it is confirmed that the proximity of transit
stations will promote walk-access trips compared to auto access trips. This result is
consistent with previous studies (Park, Choi, & Lee, 2015; Cervero, 2001). Furthermore,
living close to a transit station will also lower the odds of driving relative to biking and
45
walking. One possible explanation is that high quality transit systems are often
accompanied with increased built environment factors and network connectivity; such
as TODs.
In conclusion, all the significant variables in the basic model are consistent with
previous studies.
4.2 Partially Expanded Model with BE at Trip Origin
The inclusion of the BE variables at trip origin increased the overall predictability.
The statistical test found that BE variables had no effect on mode choice and that three
out of the four BE variables had a significant chi-square value of 50.1 with 12 degrees of
freedom. The critical χ2 with 12 degrees of freedom at 0.001 level of significance is
32.9. Thus, the null hypothesis can be rejected at very high levels. In this model, all
variables, except the transit service variable, retained their signs and significance when
being compared to the basic model.
At trip origin, the coefficient of connectivity on walking is significant and positive,
which means that higher connectivity will increase the probability of walking. A
combination of the significant impacts on the PNR mode with a negative sign and the
insignificance on WNR suggests that improving the connectivity in residential
neighborhood is effective in reducing auto access. Similarly, the outcomes of intensity
on WNR and PNR indicate that compactness within residential neighborhoods might
increase access to walking versus auto usage (PNR and drive). These results confirm
the concept of TOD in residential neighborhood, as it clearly supports that compact
development in residential areas significantly increases walking and WNR.
All the coefficients for the transit service variable lost their significance in this
model. One possible explanation is that the transit service level is related with BE
46
variables; which have a stronger weight than transit service level. Notably, the inclusion
of additional variables that represent the interaction of transit service level with BE
variables at trip origin failed to improve the model significantly. None of the interaction
variables showed as significant. This result indicates that the impact of BE variables at
trip origin are the same irrespective of whether frequent transit station are within a
quarter mile radius of a household or not.
4.3 Fully Expanded Model with BE at Trip Destination
After adding the BE variables at trip destination, the fully expanded model
improved the goodness of fit (the higher adjusted rho-squared). This model resulted in a
high chi-squared value of 192.8 and therefore, the hypothesis that BE variables at trip
destination had no effect on mode choice could be rejected at 0.001 level of
significance.
Compared to the partially expanded model, all variables except the education
level on WNR, travel time and PNR retained their significance and signs.
At trip destination, the impacts of connectivity and mixed-use on walking are
surprisingly negative and inconsistent with past findings (Cervero, 2002; Rajamani,
Bhat, Handy, Knaap, & Song, 2003; Cervero & Duncan, 2003). We will revisit the issue
when results from next model are discussed. The result was discussed in the last
section of the chapter. Notably, all four coefficients of intensity were found to be
significant at the 0.001 significance level, which suggests that high density at trip
destination has a positive impact on discouraging the decision to use a car.
4.4 Model with Trip Chaining as Interaction Term
To test if there is a difference in the impact of BE variables at trip destination on
mode choice with respect to trip chaining, additional variables were added to the fully
47
expanded model. The test resulted in a chi-squared value of 30.5 with 12 degrees of
freedom. Therefore, it confirms that the null hypothesis could be rejected at the 0.005
significance level.
Compared to the previous model, all the variables retained their significance and
signs, except for work trip on walking, trip chaining on WNR and intensity on walking.
According to probabilistic choice theory, the utility function of mixed-use level on
walking is shown as:
Utility Function (walk)=…+β1× MixUse + β2× (MixUse × Is ChainTrip)+… (4-1)
The coefficient for mixed-use level on walking is -2.21 and the interaction
between trip chaining and mixed-use level variables on walking is 2.62. Based on
Equation 4-1 the utility function of mixed-use level for non-chain trips is shown as:
Utility(walk)=…+(-2.21) × MixUse + 2.62 × (MixUse ×0) = …+(-2.21) × MixUse+… (4-2)
While for chain trips, the utility function of mixed-use level on walking is shown as:
Utility(walk)=…+(-2.21) × MixUse + 2.62 × (MixUse ×1) = …+0.41 × MixUse+… (4-3)
Therefore, the negative sign in Equation 4-2 shows that for non-chaining trips,
high instances of mixed-use development at trip destination will discourage walking.
The positive sign in Equation 4-3 indicates that for chain trips, high instances of mixed-
use development at trip destination will increase the probability of walking compared to
driving. Similarly, high-levels of connectivity at trip destination will discourage walking
for non-chain trips, but will increase the probability of walking versus driving for chain
48
Table 4-1. Model Results
Basic Model Partially
Expanded model
Fully Expanded
model
Model with Interaction
Term
Variables Coef. t Coef. t Coef. t Coef. t
Bike: Constant -1.33 -2.3* -1.01 -1.66 -1.13 -1.75 -0.88 -1.34
PNR: Constant -3.69 -3.37*** -3.51 -2.86** -4.2 -2.93** -4.12 -2.64**
Walk: Constant 2.25 4.14*** 2.57 4.51*** 2.92 4.81*** 3.23 5.22***
WNR: Constant 1.84 3.16** 2.26 3.68*** 1.38 2.03* 1.38 1.97*
drive: Travel Time
-0.22 -9.26*** -0.21 -8.85*** -0.1 -3.81*** -0.1 -3.84***
Bike: Travel Time
-0.18 -9.84*** -0.17 -9.39*** -0.11 -5.53*** -0.11 -5.64***
PNR: Travel Time
-0.05 -3.66*** -0.05 -3.91***
Walk: Travel Time
-0.14 -18.81*** -0.14 -18.43*** -0.13 -16.54*** -0.13 -16.53***
WNR: Travel Time
-0.08 -8.26*** -0.08 -7.71*** -0.04 -3.36*** -0.04 -3.34***
Bike: Bike per Person
0.65 5.88*** 0.65 5.83*** 0.62 5.57*** 0.62 5.58***
Bike: Household Size
-0.38 -4.71*** -0.35 -4.24*** -0.34 -4.02*** -0.34 -3.98***
Walk: Household Size
-0.26 -3.51*** -0.24 -3.04** -0.25 -3.17** -0.25 -3.15**
WNR: Household Size
-0.65 -7.47*** -0.61 -6.87*** -0.62 -6.62*** -0.62 -6.63***
Bike: Gender (Male=1)
0.79 5.12*** 0.78 5.05*** 0.72 4.67*** 0.74 4.75***
Bike: Education Level
0.37 5.68*** 0.36 5.53*** 0.36 5.49*** 0.37 5.61***
walk: Education Level
0.23 4.4*** 0.21 3.86*** 0.22 4.04*** 0.24 4.24***
WNR: Education Level
0.16 2.83** 0.15 2.64**
Bike: Vehicle per Person
-1.86 -7.3*** -1.78 -6.98*** -1.74 -6.79*** -1.73 -6.72***
Walk: Vehicle per Person
-1.03 -4.84*** -1.04 -4.82*** -1.1 -5*** -1.11 -5***
WNR: Vehicle per Person
-2.3 -8.7*** -2.14 -8.05*** -2.24 -8.05*** -2.24 -8.03***
*the coefficient is significant at the 0.05 level. ** the coefficient is significant at the 0.01 level. ***the coefficient is significant at the 0.001 level.
49
Table 4-1. Continued
Basic Model Partially
Expanded model
Fully Expanded
model
Model with Interaction
Term
Variables Coef. t Coef. t Coef. t Coef. t
Bike: Age -0.24 -3.98*** -0.25 -4.04*** -0.24 -3.86*** -0.25 -4.02***
PNR: Age -0.25 -2.2* -0.3 -2.51* -0.37 -2.79** -0.38 -2.83**
Walk: Age -0.16 -2.92** -0.15 -2.8** -0.14 -2.48* -0.16 -2.77**
WNR: Age -0.2 -3.59*** -0.22 -3.94*** -0.21 -3.51*** -0.21 -3.56***
Bike: Work trip (True=1)
1.28 7.62*** 1.28 7.59*** 1.21 7.06*** 1.22 7.09***
PNR: Work trip (True=1)
2.23 4.89*** 2.29 4.85*** 2.02 4.13*** 1.96 3.98***
Walk: Work trip (True=1)
0.4 2.11* 0.44 2.26* 0.39 1.96*
WNR: Work trip (True=1)
0.94 5.63*** 0.97 5.79*** 0.76 4.31*** 0.76 4.3***
Bike: Transit service (high level=1)
0.45 2.77**
Walk: Transit service (high level = 1)
0.51 3.42***
WNR: Transit service (high level = 1)
0.43 2.57*
Bike: Chain trip (True=1)
-0.34 -2.2* -0.34 -2.21* -0.41 -2.63** -1.3 -3.09**
Walk: Chain trip (True=1)
-1.08 -7.02*** -1.09 -6.98*** -1.14 -7.15*** -2.26 -5.87***
WNR: Chain trip (True=1)
-0.6 -3.76*** -0.59 -3.64*** -0.7 -4.13***
Built Environment Factors at Trip Origin
PNR: Connectivity at Oa
-0.46 -2.64** -0.52 -2.79** -0.56 -2.89**
Walk: Connectivity at Oa
0.44 3.3*** 0.65 4.18*** 0.67 4.29***
WNR: Intensity at Oa
0.87 4.64*** 0.94 4.89*** 0.93 4.83***
*the coefficient is significant at the 0.05 level. ** the coefficient is significant at the 0.01 level. ***the coefficient is significant at the 0.001 level. aO presents Origin
50
Table 4-1. Continued
Basic Model Partially
Expanded model
Fully Expanded
model
Model with Interaction
Term
Variables Coef. t Coef. t Coef. t Coef. t
Built Environment Factors at Trip Destination
Walk: Mix-use Level at Db
-1.51 -3.39*** -2.21 -4.31***
Walk: Connectivity at Db
-0.35 -3.41*** -0.45 -4.3***
Bike: Intensity at Db 0.29 3.87*** 0.19 2.01*
PNR: Intensity at Db 0.8 5.99*** 0.87 4.49***
Walk: Intensity at Db
0.32 3**
WNR: Intensity at Db
0.67 9.76*** 0.6 7.15***
Interaction Terms
Walk: Mix-use Level at Db* ChainTrip
2.62 2.68**
Walk: Connectivity at Db* ChainTrip
0.54 2.18*
Walk: Intensity at Db * ChainTrip
0.41 2.24*
Summary statistics
Log-Likelihood -1921.5 -1896.5 -1800 -1784.8
McFadden R2 0.4033 0.4111 0.441 0.4458
Adjusted R2 0.391 0.395 0.421 0.422
Likelihood Test Ratio 2597.4 2647.5 2840.3 2870.8
*the coefficient is significant at the 0.05 level. ** the coefficient is significant at the 0.01 level. ***the coefficient is significant at the 0.001 level. aO presents Origin bD presents Destination trips. The negative sign present between intensity at trip destination and trip-chaining on
walking means that high density will promote walking for chain trips.
In conclusion, the interaction terms with intuitive sign on the walk mode reveal
that the impact of BE variables at trip destination are different in respect to trip chaining
behavior.
51
CHAPTER 5 DISCUSSION
This section will discuss the major findings of this study. First, this research
reveals that the built environment in Portland generally displayed different degrees of
significance for individual mode choice at both trip origin and destination. The partially
expanded model found that improving connectivity and intensity at trip origin would
promote transit access trips. The built environment variables at trip origin might be
particularly important determinants for transit access mode choice, which is consistent
with previous literature (Cervero, 2002; Steiner, 1994; Cervero, 2001; Loutzenheiser,
1997). Compared to the built environment factors, intensity at trip destination was found
to have the strongest influence in decreasing car-dependency, with positive coefficients
on all the non-auto modes at the high significance level (PNR, WNR, walking and
biking). Notably, the chi-squared value resulted by adding BE factors at trip destination
(192.8) is much higher than that by adding BE factors at trip origin trip (50.1). In terms of
this stronger statistical fit, the study found that the quality of the built environment at trip
destination outweighed that at the residential neighborhood level; which is consistent
with a variety of studies With respect to the policy suggestion on the relationship
between land-use and transportation, the study provides supporting evidence for the
notion that urban planners should pay attention to the characteristics of non-residential
areas, such as downtowns, job centers, and other parts of the urban area.
Second, the interaction terms with predictive promise on the walk mode revealed
that the impact of BE variables at trip destination are different on the decision to walk
with respect to trip chaining behavior. The models with interaction terms found for chain
trips, high-levels of land-use diversity, connectivity and density at trip destination all
52
increased the probability to walk versus drive. This revealed that there are some
underlying relationships between trip chaining and mode choice. As walking distance is
the most significant factor in the choice to walk (Loutzenheiser, 1997; Handy S. L.,
1996), it is likely that trip chaining will discourage walking due to increased trip length.
However, when people chain ends, people prefer walking for they do not need to park.
Therefore, it can be concluded that: 1) High diversity promotes trip chaining at the trip
destination, thereby increasing the probability of walking; 2) A pedestrian-friendly
environment with high-levels of compactness and connectivity will increase the
propensity to chain by foot in a diverse setting. Based on these findings, we might
surmise that density and connectivity work together to promote walking. These findings
support Cervero’s study (1997) that it is more effective to encourage walking when
density and connectivity co-exist.
In contrast, the impact of mixed-use levels and connectivity at trip destination on
walking is negative for non-chained trips. Two possible explanations for this finding
come to mind. First, as shown in Figure 5-1, the mean distance of non-chain trips by
foot is the smallest. If people are not going to chain at trip destination, it is likely that
travel distance will be highly valued by travelers; outweighing the built environment.
Second, as trip origin inches closer to trip destination, the built environment at both trip
ends becomes very similar. In fact, the mean walking distance for non-chain trip is 0.37
mile, very close to the buffer distance used to measure the built environment factors.
Therefore, it is likely that for those shorter distance trips, travelers’ decision to walk will
depend less on the built environment at trip destination.
53
Figure 5-1. Mean Walking Distance by Trip Chaining and Mode
54
CHAPTER 6 CONCLUSION AND FUTURE RESEARCH
6.1 Conclusion
This study investigated the impact of built environment factors at trip origin and
destination in shaping mode choice using the 2011 Oregon Household Travel Survey in
Portland, Oregon, which is important because it supplements current research which
has focused primarily on residential neighborhood. In addition, the study measured the
marginal contributions of not only built environment factors, but their interaction with
transit service level and trip chaining behavior using the utility-based model.
The modeling results analyzed answered the question of whether the built
environment variables displayed different degrees of significance for individual mode
choice in terms of whether they were measured at trip origin or destination. The
outcomes confirmed the existence of such differences after controlling for attributes of
alternatives and socio-economic characteristics in Portland, Oregon. The major findings
are as follows:
1. Built environment variables at trip origin are particularly important determinants
for transit access mode choice, as high levels of connectivity and density at trip origin
significantly promote WNR over PNR.
2. Compared to other built environment variables, intensity at trip destination exerts
the strongest influence in decreasing car-dependency; with significant and positive
coefficients for all non-auto modes.
3. Because of the stronger goodness of fit, this study found that the quality of the
built environment at trip destination outweighed that at the residential neighborhood
level. This suggests that urban planners should pay attention to the characteristics of
55
nonresidential areas. Further, it is assumed that improved compactness in primarily
nonresidential areas will be most successful in reducing car sprawl.
Additional insight on the analysis is as follows: first, the study found that
collective factors extracted from the factor analysis have stronger impacts than
individual factors. Factor analysis is a helpful tool that removes common attributes
hidden within the built environment variables and reduces the negative impacts of high
multicollinearity. The results show that the two collective factors, connectivity and
intensity, explain travel demand more so than any other individual factors used in the
models. Additionally, mixed-use development level was the only significant individual
variable in the models and was proven to be the most effective measurement of
diversity on the dimension of diversity.
Second, the results indicated that the impact of the built environment at trip origin
is the same irrespective of whether transit station are within a quarter mile radius of a
household or not. It is a good indication that the impact of the built environment on TOD
residents’ decision to use WNR might not be reduced compared to non-TOD residents,
even though the distance to the station is shorter.
Last but not least, the empirical analysis suggests that mixed-use development,
connectivity and intensity at trip destination have the potential to promote walking for
chain trips. As people take advantage of the destination’ diversity, high mixed-use
development will encourage people to chain trip ends; thereby promoting walking.
Additionally, high density and connectivity will further increase the propensity to walk by
decreasing travel distance.
56
6.2 Future Research
The current study could be improved in many ways. First, attitudinal and lifestyle
preference variables could be included. For example, the inclusion of residential
location choice decisions would help discover the “true” causal impact of the built
environment on mode choice. Unfortunately, this information is not available for
Portland. Second, compared to a cross-sectional study, longitudinal data would
enhance the causal relationships of land-use and transportation. Therefore, the findings
of this study, using cross-sectional data, are interpreted as associative. Third, because
the study lacks parking information, it is impossible to enrich the model predictors with
parking availability and parking price at trip destination. As many previous studies have
proven, the existence of significant impacts of parking on an individual’s mode choice
could easily improve upon the explanatory power of the models. Lastly, the significant
relationship between trip chaining and individual mode choice suggests that more
attention should be paid on trip chaining behavior in mode choice analysis. The study
explained that the built environment at trip destination promotes walking because
people will chain in diverse settings. The explanation could be further enhanced if data
was provided on whether people chained around the destination or not. Therefore, tour-
based analysis is suggested to understand the connection between land-use and travel
behavior more deeply.
57
LIST OF REFERENCES
1000 Friends of Oregon. (1993). Making the Land Use Transportation Air Quality Connection. Portland.
Aditjandra, P. T., Mulley, C., & Nelson, J. (2013). The influence of neighbourhood
design on travel behaviour: Empirical evidence from North East England.
Transport Policy, 54–65.
Bhat, C., Sen, S., & Eluru, N. (2009). The impact of demographics, builtenvironment
attributes, vehicle characteristics, and gasoline prices on household vehicle
holdings and use. Transportation Research Part B, 43, pp. 1-18.
Boarnet, M. G., & Sarmiento, S. (1996). Can land-use policy really affect travel behavior?
A study of the link between non-work travel and land-use characteristic. Berkeley:
The University of California Transportation Center.
Boarnet, M., Anderson, C., Day, K., McMillan, T., & Alfonzo, M. (2003). Safe Routes to
School: Report to the legislature. Sacramento: California Department of
Transportation.
Boarnet, M., Greenwald, M., & McMillan, T. (2008). Walking, urban design,and health:
Toward a cost-benefit analysis framework. Journal of Planning Education and
Research(27), pp. 341–358.
Cervero, R. (2001). Walk-and-ride: factors influencing pedestrian access to transit.
Journal of Public Transportation, 3(4), pp. 1-23.
Cervero, R. (2002). Built environments and mode choice: toward a normative framework.
Transportation Research Part D, 7, pp. 265-284.
Cervero, R., & Duncan, M. (2003). Walking, Bicycling, and Urban Landscapes:Evidence
from the San Francisco Bay Area. American Journal of Public Health, 93(9), pp.
1478–1483.
Cervero, R., & Gorham, R. (1995). Commuting in transit versus automobile
neighborhoods. Journal of the American Planning Association, 61(2), 210-225.
Cervero, R., & Kockelman, K. (1997). Travel demand and the 3Ds : density, diversity,
and design. Transportation research. Part D, 2(3), 199-219.
Cervero, R., & Radlsch, C. (1995). Travel choices in pedestrian versus automobile
oriented neighborhoods. Berke]ey,: The University of California Transportation
Center.
Cervero, R., Sarmiento, O., Jacoby, E., Gomez, L., & Meiman, A. (2009). Influences of
built environments on walking and cycling: Lessons from Bogota. International
Journal of Sustainable Transportation, 3, pp. 203–226.
58
Chatman, D. G. (2003). How Density and Mixed Uses at the Workplace Affect Personal
Commercial Travel and Commute Mode Choice. Transportation Research
Record, 1831, pp. 193-201.
Chen, C., Gong, H., & Paaswell, R. (2008). Role of the built environment on mode
choice decisions: additional evidence on the impact of density. Transportation:
Planning, Policy, Research, Practice, 35(3), 285-299.
Dyck, Dyck, D., Cardon, G., Deforche, B., Sallis, J., Owen, N., & Bourdeaudhuij, J.
(2010). Neighborhood SES and walkability are related to physicalactivity
behavior in Belgian adults. Preventive Medicine, 50, pp. 74–S79.
Ewing, R., & Cervero, R. (2010). Travel and the built environment : a meta-analysis.
Journal of the American planning association, 76(3), 1-30.
Ewing, R., Schroeer, W., & Greene, W. (2004). School location and student travel:
Analysis of factors affecting mode choice. Transportation Research Record, 1895,
55-63.
Fang, J. (2016). Evaluating Neighborhood Pedestrian Environments’ Impacts on Travel
Patterns of TOD Programs in Portland City. Gainesville: Univeristy of Florida.
Frank, L. D., & Pivo, G. (1994). Impacts of Mixed Use and Density onUtilization of Three
Modes of Travel:Single-Occupant Vehicle, Transit, andWalking.Transportation
Research Record, 44-52.
Frank, L., Bradley, M., Kavage, S., Chapman, J., & Lawton, T. (2008). Urban form,
travel time, and cost relationships with tour complexity and mode choice.
Transportation, 35(1), pp. 37-54.
Friedman, B., Gordon, S., & Peers, J. (1994). The effect of neotraditional neighborhood
design on travel characteristics. Transportation Research Record, 1466, 63-70.
Handy, S. (1996). Understanding the Link between Urban Form and Nonwork Travel
Behavior. Journal of Planning Education and Research, 15, 183-198.
Handy, S. L. (1996). Urban Form and Pedestrian Choices:Study of Austin
Neighborhoods. Transportation Research Record, 1552, 135-144.
Handy, S., Xing, Y., & Buehler, T. (2010). Factors Correlated with Bicycle Commuting: A
Study in Six Small U.S. Cities. Transportation: Planning - Policy - Research -
Practice, 37(6), pp. 967–985.
Hensher, D., & Reyes, A. (2000). Trip chaining as a barrier to the propensity to use
public transport. Transportation, 27, 341–361.
Hess, D. (2001). Effect of Free Parking on Commuter Mode Choice: Evidence from
Travel Diary Data. Transportation Research Record, 1753, pp. 35-42.
Jacobs, J. (1992). The Death and Life of Great American Cities. U.S.: Vintage.
59
Kelbaugh, D. (2002). Repairing the American Metropolis: Common Place Revisited.
University of Washington Press.
Lee, J.-S., Jin, N., & Lee, S.-S. (2014). Built Environment Impacts on Individual Mode
Choice: An Empirical Study of the Houston-Galveston Metropolitan Area.
International Journal of Sustainable Transportation, 8, pp. 447–470.
Lomborg, B. (2001). The Skeptical Environmentalist: Measuring the Real State of the
World. Cambridge University Press.
Loutzenheiser, D. R. (1997). Pedestrian Access to Transit:Model of Walk Trips and
Their Design and Urban Form Determinants Around Bay Area Rapid Transit
Stations. Transportation Research Record, 1604, pp. 40-49.
Lund, H. M., Cervero, R., & Willson, R. W. (2004). Travel characteristics of transit-
oriented development in California.
McGuckin, N., Zmud, J., & Nakamoto, Y. (2005). Trip-Chaining Trends in the United
States: Understanding Travel Behavior for Policy Making. Transportation
Research Record 1917, 199–204.
McKibbin, M. (2011). The influence of the built environment on mode choice: evidence
from the journey to work in Sydney. Australasian Transport Research Forum
(ATRF), 34. Adelaide.
Overly, S. (2017, January 4). Americans bought more cars than ever last year. In 2017, things could get bumpy. Retrieved from The Washington Post: https://www.washingtonpost.com/news/innovations/wp/2017/01/04/americans-bought-more-cars-than-ever-last-year-in-2017-things-could-get-bumpy/?utm_term=.09bec4a99b02
Park, S. (2008). Defining, Measuring, and Evaluating Path Walkability, and Testing Its
Impacts on Transit Users’ Mode Choice and Walking Distance to the Station
(Doctoral dissertation). Retrieved from University of California Transportation
Center.
Park, S., Choi, K., & Lee, J. (2015, July 11). To Walk or Not to Walk: Testing the Effect
of Path Walkability on Transit Users' Access Mode Choices to the Station.
International Journal of Sustainable Transportation, 9(8), pp. 529-541.
Park, S., Kang, J., & Choi, K. (2014). Finding determinants of transit users’ walking and
biking access trips to the station: A pilot case study. KSCE Journal of Civil
Engineering, 18(2), pp. 651-658.
Parks, J. R., & Schofer, J. L. (2006). Characterizing neighborhood pedestrian
environments with secondary data. Transportation Research, 250–263.
60
Rajamani, J., Bhat, C. R., Handy, S., Knaap, G., & Song, Y. (2003). Assessing impact of
urban form measures on nonwork trip mode choice after controlling for
demographic and level-of-service effects. Transportation research record, 1831,
158-165.
Reilly, M., & Landis, J. (2002). The influence of built form and land use on mode choice.
Berkeley: Univeristy of California Transportation Center.
Steiner, R. (1994). Residential density and travel patterns: review of the literature1466.
Transportation Research Record, 43–47.
Xu, R. (2014). How the built environment affects elderly travel behavior : an activity-
based approach for Southeast Florida. Gainesville: University of Florida.
Yang, R., Yan, H., Xiong, W., & Liu, T. (2013, November 6). The Study of Pedestrian
Accessibility to Rail Transit Stations based on KLP Model. Social and Behavioral
Sciences, 96, pp. 714-722.
Zhang, L., Hong, J., Nasri, A., & Shen, Q. (2012). How built environment affects travel
behavior: A comprehensive analysis of the connections between land use and
vehicle miles traveled in U.S. cities. The Journal of Transport and Land Use, 5(3),
40-52.
Zhang, M. (2004). The Role of Land Use in Travel Mode Choice:Evidence from Boston
and Hong Kong. Journal of the American Planning Association, 70(3), pp. 344-
360.
Zhou, B., & Kockelman, K. ( 2008). Self-selection in Home Choice: Use of treatment
Effects in Evaluation the Relationship between the Built Environment and Travel
Behavior. Transportation Research Record, 2077, pp. 54–61.
61
BIOGRAPHICAL SKETCH
Jia Fang is from China and attended the University of Sun Yet-Sen from 2006 to
2010, graduating with a bachelor’s degree in geo-information science and technology.
In 2013, she received her master’s degree in geographical information systems (GIS) at
Zhejiang University. From 2015 to 2017, she was pursuing her second master’s degree
in urban and regional planning at the University of Florida with an interest in
transportation planning. She has worked as a transit planning intern for the City of
Gainesville’s Regional Transit System since January 2017. Jia is preparing herself for
improving the environment people travel in.