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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

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.

Suggested Citation

  • Al-Deek, Haitham M. & Khattak, Asad J. & Thananjeyan, Paramsothy, 1998. "A combined traveler behavior and system performance model with advanced traveler information systems," Transportation Research Part A: Policy and Practice, Elsevier, vol. 32(7), pages 479-493, September.
    • Handle: RePEc:eee:transa:v:32:y:1998:i:7:p:479-493
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    References listed on IDEAS

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    Cited by:

    1. Zhang, Rong & Verhoef, Erik T., 2006. "A monopolistic market for advanced traveller information systems and road use efficiency," Transportation Research Part A: Policy and Practice, Elsevier, vol. 40(5), pages 424-443, June.
    2. 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.
    3. David Levinson, 2003. "The Value of Advanced Traveler Information Systems for Route Choice," Working Papers 200307, University of Minnesota: Nexus Research Group.
    4. 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.
    5. 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.
    6. 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.
    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.
    8. Thill, Jean-Claude & Rogova, Galina & Yan, Jun, 2004. "Evaluating Benefits And Costs Of Intelligent Transportation Systems Elements From A Planning Perspective," Research in Transportation Economics, Elsevier, vol. 8(1), pages 571-603, January.
    9. Rodríguez, Daniel A. & Levine, Jonathan & Agrawal, Asha Weinstein & Song, Jumin, 2011. "Can information promote transportation-friendly location decisions? A simulation experiment," Journal of Transport Geography, Elsevier, vol. 19(2), pages 304-312.
    10. Jihao Deng & Lei Gao & Xiaohong Chen & Quan Yuan, 2024. "Taking the same route every day? An empirical investigation of commuting route stability using personal electric vehicle trajectory data," Transportation, Springer, vol. 51(4), pages 1547-1573, August.
    11. Bifulco, Gennaro N. & Cantarella, Giulio E. & Simonelli, Fulvio & Velonà, Pietro, 2016. "Advanced traveller information systems under recurrent traffic conditions: Network equilibrium and stability," Transportation Research Part B: Methodological, Elsevier, vol. 92(PA), pages 73-87.

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