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A combined traveler behavior and system performance model with advanced traveler information systems

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  • Al-Deek, Haitham M.
  • Khattak, Asad J.
  • Thananjeyan, Paramsothy
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    Abstract

    The goal of this paper is to develop a framework for evaluating the effect of Advanced Traveler Information Systems. The framework uses a composite traffic assignment model which combines a probabilistic traveler behavior model of route diversion and a queuing model to evaluate Advanced Traveler Information Systems impacts under incident conditions. The composite assignment model considers three types of travelers: those who are unequipped with electronic devices, i.e. they do not have Advanced Traveler Information Systems or radio in their vehicles; those who receive delay information from radio only; and those who access Advanced Traveler Information Systems only. The unequipped travelers are able to observe incident-induced congestion, if the congestion reaches or exceeds their decision point. The composite model assigns travelers with Advanced Traveler Information Systems to the shortest travel time route. Travelers with radio information and those who can observe the congestion are assigned according to a behavioral model calibrated on revealed preference data. Travelers who are completely unaware of the incident-induced congestion are assigned to their usual route. The unique feature of the composite model is the integration of realistic traveler behavior with system performance while accounting for the effect of real-time travel information. To demonstrate the application of the composite model, we consider the evolution of queues on a two link network with an incident bottleneck. The findings indicate that the overall system performance, measured by average travel time, improves marginally with increased market penetration of Advanced Traveler Information Systems. However, the benefits of Advanced Traveler Information Systems under incident conditions are expected to be marginal when there is more 'information' available to travelers through their own observation or radio. Specifically, delay information received through radio and from observation of incident-induced congestion induces people to divert earlier causing the network to operate closer to system optimal than user equilibrium. This limits the potential benefits of Advanced Traveler Information Systems.

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    Bibliographic Info

    Article provided by Elsevier in its journal Transportation Research Part A: Policy and Practice.

    Volume (Year): 32 (1998)
    Issue (Month): 7 (September)
    Pages: 479-493

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    Handle: RePEc:eee:transa:v:32:y:1998:i:7:p:479-493

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    1. Gardes, Yonnel & May, Adolf D., 1990. "Traffic Modeling To Evaluate Potential Benefits Of Advanced Traffic Management And In-vehicle Information Systems In A Freeway/Arterial Corridor," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt70c6600f, Institute of Transportation Studies, UC Berkeley.
    2. Gardes, Yonnel & Haldors, Bruce & May, Adolf D., 1991. "Model Selection And Initial Application Of CONTRAM Model For Evaluating In-vehicle Information Systems," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt37s9526g, Institute of Transportation Studies, UC Berkeley.
    3. Mannering, Fred L., 1989. "Poisson analysis of commuter flexibility in changing routes and departure times," Transportation Research Part B: Methodological, Elsevier, vol. 23(1), pages 53-60, February.
    4. Kanafani, A. & Al-Deek, H., 1991. "A simple model for route guidance benefits," Transportation Research Part B: Methodological, Elsevier, vol. 25(4), pages 191-201, August.
    5. Arnott, R. & de Palma, A. & Lindsey, R., 1990. "Departure time and route choice for the morning commute," Transportation Research Part B: Methodological, Elsevier, vol. 24(3), pages 209-228, June.
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    Cited by:
    1. Caspar Chorus & Theo Arentze & Harry Timmermans, 2007. "Information impact on quality of multimodal travel choices: conceptualizations and empirical analyses," Transportation, Springer, vol. 34(6), pages 625-645, November.
    2. Rong Zhang & Erik T. Verhoef, 2004. "A Monopolistic Market for Advanced Traveller Information Systems and Road Use Efficiency," Tinbergen Institute Discussion Papers 04-014/3, Tinbergen Institute.
    3. Lo, Hong K. & Szeto, W. Y., 2004. "Modeling advanced traveler information services: static versus dynamic paradigms," Transportation Research Part B: Methodological, Elsevier, vol. 38(6), pages 495-515, July.
    4. Coifman, Benjamin A. & Mallika, Ramachandran, 2007. "Distributed surveillance on freeways emphasizing incident detection and verification," Transportation Research Part A: Policy and Practice, Elsevier, vol. 41(8), pages 750-767, October.
    5. David Levinson, 2003. "The Value of Advanced Traveler Information Systems for Route Choice," Working Papers 200307, University of Minnesota: Nexus Research Group.
    6. Enrique Fernández L., J. & de Cea Ch, Joaquín & Germán Valverde, G., 2009. "Effect of advanced traveler information systems and road pricing in a network with non-recurrent congestion," Transportation Research Part A: Policy and Practice, Elsevier, vol. 43(5), pages 481-499, June.
    7. Levinson, David & Gillen, David & Chang, Elva, 1999. "Assessing the Benefits and Costs of Intelligent Transportation Systems: The Value of Advanced Traveler Information Systems," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt9m8534tc, Institute of Transportation Studies, UC Berkeley.

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