ENHANCING INTELLIGENT TRANSPORTATION SYSTEMS APPLICATIONS IN URBAN TRANSPORTATION THROUGH GIS

Ata M. KHAN Professor

Department of Civil and Environmental Engineering Carleton University

1125 Colonel By Drive Ottawa, Ontario K1S 5B6

Canada Tel: +01 613 520 2600 (5786)

Fax: +01 613 520 3951 E-mail: ata_khan@carleton.ca

Jennifer M. ARMSTRONG

Ph.D. Student Department of Civil and Environmental Engineering

Carleton University Transportation Engineer, Morrison Hershfield

2440 Don Reid Dr. Ottawa, Ontario K1H 1E1

Canada Tel: +01 613 739 2910 Fax: +01 613 739 4926

E-mail: jarmstrong@morrisonhershfield.com Abstract: Intelligent Transportation Systems (ITS) have the potential to enhance efficiency and safety and also reduce energy consumption and emissions in urban transportation. In many instances, ITS applications require detailed spatial data about infrastructure, control systems, vehicles, and the demand for transportation, which can be acquired with assistance from the technologically advanced components of Geographic Information Systems (GIS). Although most urban areas in industrially advanced countries appear to be incorporating ITS in their system improvements, there is a lack of appreciation about the linkages between ITS and GIS. The objectives of this paper are to (1) describe the state of development of Intelligent Transportation Systems (ITS) that can enhance urban transportation, (2) identify technological and other developments in GIS that can be combined with ITS, and (3) present examples of ITS applications in urban transportation that can be enhanced in a GIS environment.

Keywords: Intelligent Transportation Systems, Urban transportation, Geographic Information Systems, INTEGRATION, simulation

Paper 106 1

ENHANCING INTELLIGENT TRANSPORTATION SYSTEMS APPLICATIONS IN URBAN TRANSPORTATION THROUGH GIS

1 INTRODUCTION Intelligent Transportation Systems (ITS) have reached a stage in their development path where they can now be effectively used to increase efficiency, enhance safety, and also reduce energy consumption and emissions in urban transportation (Maccubin et al. 2003). However, before investment decisions can be made, potential ITS applications need to be subjected to comprehensive analysis and evaluation. Methodologies for performing these tasks require detailed spatial data about infrastructure, control systems, vehicles, and the demand for transportation, which can be obtained using the technologically advanced components of GIS (Khan, Taylor and Armstrong 2004).

Most urban areas in industrially advanced countries appear to be taking steps to incorporate ITS in their system improvement plans. However, the examples reported in the literature give the impression that there is a lack of appreciation regarding the linkages between ITS and GIS. Part of the problem may be a scarcity of literature that can be accessed by urban transportation planners and managers. Likewise, specialists knowledgeable in GIS may not be aware of the potential applications of ITS in urban transportation and the potential enhancement of ITS when combined with GIS.

As a contribution to the current knowledge-base on ITS and GIS, this paper describes a case study involving the use of GIS to develop a traffic microsimulation model for analyzing ITS applications with the potential to enhance urban transportation, and in particular, traffic operations on urban freeways. By way of background material, the paper introduces ITS and outlines technological developments in GIS that can be combined with ITS. However, the main focus is on how ITS applications in urban transportation can be analyzed using the capabilities of GIS.

The paper consists of five parts. Following the introductory section, highlights of ITS technologies are presented that can potentially enhance urban transportation. Spatial information and computing requirements for the analysis and evaluation of such systems are then identified. Next, technological and other advances in GIS are described that are relevant to ITS applications. Following this general description of ITS and GIS, a detailed study of potential ITS applications in Ottawa (Canada) is presented. Based on the findings from this analysis, conclusions are presented in the final section of the paper.

2 INTELLIGENT TRANSPORTATION SYSTEMS

2.1 Technologies and Services A number of definitions of ITS have been advanced. For example, according to Transport Canada (2003), ITS refer to the integrated application of information processing, communications, and sensor technologies, to transportation infrastructure and operations. These systems bring together users, vehicles and

Paper 106 2

infrastructure into a dynamic relationship of information exchange, resulting in better management strategies and more efficient use of available resources.

In general, Intelligent Transportation Systems are comprised of four key elements: the vehicle, the user, the infrastructure, and the communications system (Transport Canada 1999). Figure 1 illustrates these elements, and their interaction with each other.

Each of the components identified in Figure 1 is associated with a range of advanced technologies which interact in various ways to enhance the delivery of transportation services. Communications technologies foster this interaction through the exchange of information. Vehicle and infrastructure technologies support real-time monitoring and control functions, as well as numerous other applications related to the delivery and management of intelligent transportation services. These technologies are key to successful ITS deployment, and support a range of user services aimed at improving the safety and efficiency of the transportation system and reducing environmental impacts.

Intelligent vehicle initiative

Smart infrastructure

User servicesHuman factors

Communications systems

Figure 1 Components of Intelligent Transportation Systems

ITS technologies require detailed analysis in order to assess their feasibility for application in urban public transportation, road traffic systems and intermodal services. Transportation agencies have already started to implement certain ITS products and research studies are underway to examine the viability and potential benefits of novel technologies and products (Khan and Tayyaran 2004, Taylor and Khan 2003). Likewise, there is a keen interest in the profession in using ITS to improve urban traffic control and management, and intermodal transportation (Canadian Atlantic Network 2004). Our aim in this paper is to investigate selected ITS applications in urban traffic management.

2.2 Methodological Requirements Detailed analysis and evaluation of ITS applications is needed to support investment decision-making. Given that most ITS technologies are intended to improve traffic operations, coarse macro-level planning tools are of limited utility, since such tools do not capture interaction between individual vehicles and are therefore insensitive to operational-level projects such as ITS. As a result, the use of specialized microsimulators with the capability to model ITS technologies is necessary for

Paper 106 3

assessing ITS applications in transportation.

Microsimulators have extensive data requirements, and rely on detailed information on network geometry and traffic flow characteristics to estimate network performance. In order to prepare the various inputs required for microsimulation, the use of GIS and related software is highly desirable.

In addition to supporting microsimulation studies, GIS provides an effective framework for analyzing many different types of transportation issues, ranging from network planning to routing and optimization studies. The role of GIS in transportation system planning and management is briefly described in the section below.

3 TECHNOLOGICAL DEVELOPMENTS IN GIS The use of computers in mapping and spatial analysis was initiated over 40 years ago. Since then, there has been much progress both in the technology, and the diversity of applications in which the technology is used. Recent technological developments in GIS and related systems/services, with relevance to transportation, are presented in Figure 2. The developments are organized into hardware, software, web technology, and specialized combinations. Selected highlights are described below. Additional information can be found in Khan, Taylor and Armstrong (2004).

Sensor technology

GPS data

Recent technological developments in GIS & related systems

Hardware Software Web technologySpecialized

combinations

Remote sensing equipment Technology for tracking probe vehicles

Collection equipment for field studies

Combination of ITS, remote sensing & GIS

Combination of ITS, GIS and Web technology

Combination of GIS & probe vehicle method for traffic condition studies

Combination of GIS & microsimulations GIS & advanced traffic control applications

Integration of network-wide sensor data & GIS for real time traffic information

The internet computing model

Figure 2 Technological developments in GIS (Khan, Taylor and Armstrong 2004)

A recent innovation in data acquisition is the use of geospatial information technologies. These technologies are also being used as advanced tools for transportation planning and operations. To give one example, Thirumalai (2003)

Paper 106 4

advances this theme by showing how to expand ITS technology services through integration with commercial remote sensing and spatial information technologies. To carry this theme further, commercial remote sensing and spatial information technologies can be used as imagery-based tools and systems for the transportation services market.

GIS-based interfaces for advanced traffic control are also becoming available (Bargiela and Berry 1999). Such systems have many applications. For example, they can be used to monitor real-time traffic flows at selected locations in the network. Likewise, web-based systems for displaying real-time traffic flow conditions have become available (Globis Data 2004).

Technology assessments show that change in GIS technology has been rapid; it is expected that advances will continue to occur at a similar pace in the future. When combined with other factors, technology enhancements are likely to lead to the increased use of GIS in urban transportation planning, operations and management. The research described in this paper combines ITS, GIS and microsimulation.

4 MODELLING ITS

4.1 GIS and Microsimulation As Tools for Modelling ITS To evaluate the potential impacts of ITS, a case study was undertaken using MapInfo and INTEGRATION. MapInfo is a GIS software package while INTEGRATION is a microscopic traffic simulation model that is capable of tracking the movement of each individual vehicle on the road network at a resolution of one deci-second. This microscopic approach facilitates the analysis of many dynamic traffic phenomena, such as shock waves, gap acceptance, and weaving, which are often difficult to capture under non-steady state conditions using macroscopic planning and analysis models (Van Aerde 2003).

The purpose of this research was to investigate the effect of selected ITS user services on network efficiency, energy consumption, and vehicle emissions. The technologies examined include: variable message signs, traveller information and in-vehicle navigation, incident management, and toll collection. Two scenarios were analyzed: one with no incidents on the primary road network, and one with a collision on a major freeway facility closing one full lane of traffic for a period of 20 minutes. Due to space constraints, only the results of the no incident scenario are reported.

Traditionally, analysis of ITS technologies has been limited in scope, with most studies focusing exclusively on the corridor where the technology is to be implemented. However, while a corridor-level analysis is of some value in assessing the relative merit of ITS strategies, a more holistic network-level analysis is required to truly capture the implications of ITS, including diversion to alternative routes. Consequently, a decision was made to develop a network-level model based on the existing transportation environment in the City of Ottawas west urban community. The model corresponds to the afternoon peak hour in the year 2000.

4.2 Study Area The study area for the analysis is shown in Figure 3. It includes the communities of Kanata and Bells Corners, as well as a portion of the greenbelt which separates the west urban community from the central part of the city. The area has undergone

Paper 106 5

rapid development in recent years, resulting in traffic congestion on the main routes. As the area continues to grow, ITS may represent a possible solution for enhancing network efficiency and improving traffic flow.

Figure 3 Map of Study Area (Ottawa West Community, Canada)

Study Area

4.3 Overview of the Model Development Process To simulate traffic operations in INTEGRATION, a number of input files must be created. Some of the input files are optional, while others are mandatory and must be present for the model to run correctly. Table 1 contains a summary of the input files that were developed to model traffic operations in the Ottawa west community. Figure 4 provides an overview of the model development process, and shows the relationship between the various software packages that were used.

In essence, the input files fall into two distinct categories:

Files related to network characteristics Files related to travel demand The travel demand inputs were developed using QueensOD, a software package available from the developers of INTEGRATION. Unlike traditional planning models which employ trip distribution techniques to estimate origin-destination (O-D) demands, QueensOD, uses a synthetic O-D estimation procedure to develop trip tables based on observed link flows (Van Aerde 2002). QueensOD is fully compatible with INTEGRATION, and in fact relies on the same network files for generating network-specific O-D demands.

Paper 106 6

Table 1: Summary of INTEGRATION Input Files

File Status Description

Master Control File

Required Identifies the location of the remaining input files Outlines the characteristics of the assignment

process Specifies which output files are to be generated

and where the files should be located

Node File

Required Describes the location of each node in the transportation network

Identifies nodes that act as origins and/or destinations

Link File

Required Describes the characteristics of each unidirectional link in the transportation network (i.e. length, capacity, free flow speed, speed at capacity, jam density, turn prohibitions, opposing links, presence of traffic control devices, vehicle discharge characteristics, etc.)

Signal File

Required Describes the characteristics of each traffic signal in the transportation network (i.e. cycle length, offset, number of phases, etc.)

Indicates whether signal timing and offset optimization are to be performed

Origin-Destination File

Required Specifies the travel demand between each O-D pair by vehicle class

Incident File

Required Describes the location and duration of any incidents on the road network, as well as the corresponding impact on road capacity (an incident is defined as any event which blocks one or more lanes of travel for a specified period of time, such as a collision or vehicle break-down)

Lane Striping File

Optional Designates left, through, and right turn lanes for each intersection approach

Paper 106 7

Import City of Ottawa GIS data intoMapInfo (road centreline layer)

Use ITSS to create base network

Use ITSS to enter lane striping data

Use ITSS to enter link flows and turningmovements

Use ITSS to specify origin anddestination nodes

Use ITSS to modify link attributes(number of lanes, capacity, free flow

speed, speed at capacity, jam density,turn prohibitions, opposing links, etc.)

Use ITSS to specify the location oftraffic control devices

Lane StripingFile

INTEGRATION MODEL

Export data to MS Excel andmanipulate as required QUEENSOD

Node FileLink File

Signal File

Link Flow &Turning

Movement Files

OD File

ITSS

TO

OL

Figure 4 Model Development Process

Paper 106 8

The network files for INTEGRATION (and QueensOD) were developed using MapInfo. Each input file is a simple text file comprised of multiple fields of data. Given the format of the files, it is extremely time-consuming to code all but the simplest networks. To assist in the development of INTEGRATION networks, the City of Portlands Office of Transportation developed a MapInfo application called the INTEGRATION Traffic Simulation Shell (ITSS). This application runs on the MapInfo Professional desktop GIS.

Using ITSS, it is possible to quickly create INTEGRATION networks from existing street centerline files. Alternatively, a new network can be drawn in a map window. Because the system is GIS-based, aerial photographs and edge of pavement files can be placed in background layers for tracing. Network editing is done visually using simple drawing tools. The use of aerial photographs makes it particularly easy to code lane designations and the length of auxiliary turning lanes.

The GIS environment reduces the amount of data the user must maintain. Values such as node coordinates and link lengths are derived from the GIS and do not rely on user input, reducing the potential for errors. The visual interface of the GIS also facilitates network verification exercises, ensuring the network coding is correct. Moreover, the ITSS editing tools provide network data synchronization, so that changes to one data file are automatically incorporated into any related files.

The use of GIS also makes it possible to bring together non-network related data for developing the input files, including traffic count data, and origin-destination data from regional travel surveys. In the case of the Ottawa study, traffic count data was used to develop the travel demand matrix in QueensOD. The traffic count data was linked to the appropriate road segment within the GIS environment, substantially reducing the level of effort that would have otherwise been required.

Figure 5 provides a snapshot of the MapInfo editing environment, while Table 2 contains a summary of the Ottawa model. Assumptions regarding key network characteristics can be found in Table 3.

Table 2: Network Characteristics

Network Feature Value

Number of Nodes 423

Number of Origins 104

Number of Destinations 104

Number of Links 838

Number of Traffic Signals 60

Number of Stop Signs 46

Number of Yield Signs 19

Total Network Length 259 km

Paper 106 9

Figure 5 MapInfo Editing Environment

Paper 106 10

Table 3: Summary of Key Traffic Flow Assumptions

Traffic Flow Parameters Arterial Links Freeway Links

Saturation Flow Rate 1900 pcphpl 2400 pcphpl

Free Flow Speed 60 km/hr 110 km/hr

Speed at Capacity 45 km/hr 90 km/hr

Jam Density 125 veh/km 125 veh/km

4.4 Scenarios and Results The INTEGRATION software has the ability to model ITS user services ranging from in-vehicle traveller information to incident management. To assess the impact of these user services, a number of scenarios were defined. In general, the scenarios focus on services impacting vehicle drivers. Most measures were assumed to be implemented on Highway 417, the primary east-west corridor within the study area.

The ITS scenarios that were investigated are described in Table 4. Other scenarios used for comparison purposes are shown in Table 5.

Table 4: ITS Scenarios

No. Description Modeling Approach

1 Variable message signs

Modification of traffic assignment assumptions in the vicinity of the sign to provide motorists with the opportunity to select a new route based on real-time information

2 En-route traveler information with in-vehicle navigation

Modification of traffic assignment assumptions, such that 90% of vehicles are assumed to receive real-time traffic information and navigational support to select the optimal route

3 Electronic toll collection

Development of an INTEGRATION input file for toll collection on Highway 417

4 Incident management Reduction in the duration of an incident on Highway 417 by 50% (note that this paper only presents results for the case with no incident on Highway 417)

Paper 106 11

Table 5: Other Scenarios

No. Description Modeling Approach

1 Reduced vehicle travel demand

This scenario involved a 20% system-wide reduction in vehicle travel demand. In some ways, this scenario captures ITS user services related to public transit, which are largely intended to achieve sustainability benefits by reducing reliance on private vehicles.

2 Adding lanes to Highway 417

In this scenario, Highway 417 was widened from a four-lane cross-section to a six-lane cross-section in the vicinity of the study area. This scenario provided an opportunity to assess the relative merit of ITS initiatives compared to more traditional capacity-building projects.

The results for each scenario are shown in Table 6.

Table 6: Simulation Results (Without Incident on Highway 417)

Scenarios

Measure of Effectiveness Base Case

Variable Message Signs

Traveler Information/ Navigation

Toll Collection

Reduced Demand

New Capacity

Fuel Usage (kilolitres) % Change

38.3 -

37.7 -1.6%

36.6 -4.4%

37.8 -1.3%

26.4 -31.1%

37.2 +2.9%

HC Emissions (kilograms) % Change

104.4 -

104.0 -0.4%

99.5 -4.7%

79.8 -23.6%

80.7 -22.7%

106.7 +2.2%

CO Emissions (kilograms) x 100 % Change

27.9 -

27.9 0.0%

26.6 -4.7%

19.7 -29.4%

22.5 -19.4%

29.0 +3.9%

NOX Emissions (kilograms) % Change

96.5 -

96.2 -0.3%

93.5 -3.1%

82.7 -14.3%

74.6 -22.7%

99.1 +2.7%

Total Vehicle-Hours of Travel % Change

286,661 -

277,102 -3.3%

273,847 -4.5%

274,796 -4.1%

195,280 -31.8%

263,414 -8.1%

Paper 106 12

Variable message signs are used to provide real-time information to motorists at strategically-placed locations. Based on the information provided, motorists can then choose whether or not to deviate from their planned route. The results of the traffic simulation show that total vehicle-hours of travel on the network drops by about 3% following the installation of variable message signs, fuel consumption drops slightly and virtually no change occurs in vehicle emissions.

Under the traveller information scenario, it is assumed that 90% of drivers receive en-route traveller information describing real-time traffic conditions. It is further assumed that these drivers have in-vehicle navigational systems which continually assess the optimal travel route to the drivers intended destination. While this level of technology uptake may appear somewhat aggressive, such systems are being gradually introduced in some cities around the world. If the rate of adoption mirrors that of technologies such as ABS brakes, it is not unforeseeable that such systems could one day become commonplace.

Of all the ITS measures examined, the traveller information scenario provides the greatest overall benefit in terms of travel time savings. Compared to the base case scenario, network wide travel time is reduced by nearly 5%. This level of improvement is roughly half the benefit achieved from widening Highway 417. However, unlike the road widening scenario, there is also improvement in network-wide vehicle emissions and fuel consumption.

Electronic toll collection supports the introduction of congestion pricing as a means to manage travel demand. Highway 417 currently operates as a toll-free facility. While it is not expected that toll collection will be introduced on Highway 417 in the near future, a toll collection scenario was examined to determine what the implications of such a system would be. However, the reader is advised to view the results with caution due to a number of assumptions made in the preparation of the input files.

To model the impact of toll collection on Highway 417, an average toll of $0.10 per kilometre was assumed. During the simulation of the toll collection scenario, many drivers were observed to divert to alternative arterial routes. As a result of this observed response, a travel time savings of just over 4% was achieved. It is hypothesized that this benefit is due to a more efficient use of the road network the toll on Highway 417 encourages drivers to use routes that were previously considered less optimal from an individual perspective, but which are in fact more optimal from a system-wide perspective.

In addition to producing travel time savings, the toll caused the level of vehicle emissions on the road network to decrease, while fuel consumption remained relatively unchanged. The decrease in vehicle emissions can potentially be attributed to a higher proportion of travel at arterial road speeds, which tend to be more optimal from an emissions perspective.

It is important to emphasize that, as an operational-level model, the simulator (INTEGRATION) fails to capture many of the key impacts of congestion pricing, such as variations in the mode or time of travel. As a result, the benefits could be even greater than estimated by the model, with reductions in the overall level of delay due to reduced vehicular demand during the peak hour.

As demonstrated from the results presented above, ITS has the potential to improve the efficiency of the transportation network, improving traffic operations and reducing delay. However, such operational gains may be short-lived if induced demand

Paper 106 13

materializes. For this reason, efforts to reduce travel demand are key. From the results of the simulation analysis, a 20% reduction in vehicle travel leads to both environmental and operational benefits which far exceed those of any of the other scenarios examined. However, this reduction in vehicular travel may be difficult to achieve without ITS user services which improve the quality of public transit.

The new capacity scenario simply reflects planned enhancements to Highway 417. This scenario has significant operational benefits in terms of delay reduction. However, the travel time savings come at a cost to the environment. An increase in capacity on Highway 417 leads to an increase in vehicle emissions as more vehicles travel at higher speeds outside their optimal range. This finding is contrary to many statements in the literature which imply that capacity enhancements tend to reduce vehicle emissions by improving traffic flow. Clearly, the non-linearity surrounding vehicle emissions creates significant challenges in estimating environmental impacts.

5 CONCLUSIONS Based on the results of the case study presented above, the following conclusions can be made:

(1) ITS applications in urban traffic management show promise in terms of improving operations, enhancing energy efficiency and reducing emissions. Although the best option is to reduce automobile travel demand (i.e. by encouraging the use of alternative travel modes), ITS applications compare favourably with capacity additions.

(2) To evaluate ITS applications, a simulation methodology is required. The use of such a methodology can be significantly enhanced through the use of GIS. Indeed, the ITS case study reported here could not have been carried out to the same level of detail within the same timeframe without the assistance of GIS. GIS provides an effective framework for developing microsimulation network files, verifying network coding, integrating network and non-network data such as traffic counts, and displaying simulation results.

(3) The Ottawa case study did not consider the impact of ITS on travel decisions related to the mode or time of travel. Moreover, the case study did not evaluate the potential for induced demand to materialize due to a reduction in travel time. To do so would require a macroscopic demand model for forecasting travel behaviour. This suggests the need for an integrated modelling framework which incorporates both planning and operational-level analyses. There appears to be some movement in this direction, and it is likely that advances in this regard will be forthcoming.

(4) While simulation models such as INTEGRATION provide an excellent tool for evaluating a range of operational-level improvements, such models are only one component of a broader evaluation framework which also includes cost considerations, user satisfaction, and other measures of effectiveness that go beyond purely operational considerations.

Paper 106 14

ACKNOWLEDGEMENTS

This paper is based on research sponsored by AUTO21, the Ministry of Transportation of Ontario (MTO) and the Natural Sciences and Engineering Research Council of Canada (NSERC). The opinions expressed are those of the authors.

REFERENCES

Bargiela, A. and Berry, R. (1999) Every BIT counts Enhancing the benefits of UTC through distributed applications, Traffic Technology International, February 1999.

Canadian Atlantic Network (2004) Final Report: Phase 3. Centre de recherch sur les transports, University of Montreal, May 2004.

Globis Data (2004) Development of D.R.I.V.E.S. Globus Data Inc., Montreal, Quebec. (website: www.its-sti.gc.ca/en/deployment/Quebec/DRIVEs.htm)

Khan, A.M. and Tayyaran, M.R. (2004) Risk analysis of Intelligent Transportation Systems. Proceedings of World Conference on Transport Research, Istanbul, Turkey, July 2004.

Khan, A.M., Taylor, S.J. and Armstrong, J.M. (2004) Factors affecting the use of GIS in urban transportation planning & management. Proceedings 7th International Seminar on GIS for Developing Countries, Universiti Teknologi Malaysia, 10-12, May 2004.

Maccubin, R., Staples, B.L. and Mercer, M.R. (2003) Intelligent Transportation Systems Benefits and Costs: 2003 Updates. Report No. FHWA-OP-03-075, US Department of Transportation, Washington, D.C.

Taylor, S. and Khan, A.M. (2003) Development of a public transit information system: Use of GIS and ITS technologies. Proceedings, Canadian Transportation Research Forum 2003 Annual Conference, Ottawa.

Thirumalai, K. (2003) ITS integration with commercial remote sensing and spatial information technologies. Proceedings ITS World Congress, Madrid.

Transport Canada (1999) An Intelligent Transportation Systems (ITS) Plan for Canada: En route to Intelligent Mobility. (website: http://www.its-sti.gc.ca/en/ randd/menu.htm)

Transport Canada (2003) Intelligent Transportation Systems Research and Development Plan for Canada: Innovation through Partnership. (website: http://www.its-sti.gc.ca/en/randd/menu.htm)

Van Aerde, M. & Associates Ltd. (2002) QUEENSOD Release 2.10 Users Guide: Estimating Origin-Destination Traffic Demands from Link Flow Counts. Kingston, Ontario.

Van Aerde, M. & Associates Ltd. (2003) INTEGRATION Release 2.30 for Windows: Users Guide. Volumes 1 & 2. Kingston, Ontario.

Paper 106 15

INTRODUCTIONINTELLIGENT TRANSPORTATION SYSTEMSTechnologies and ServicesMethodological Requirements

TECHNOLOGICAL DEVELOPMENTS IN GISMODELLING ITSGIS and Microsimulation As Tools for Modelling ITSStudy AreaOverview of the Model Development ProcessScenarios and Results

CONCLUSIONS