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Using a weight-assessing model to identify route choice criteria and information effects

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  • Chen, Ting-Yu
  • Chang, Hsin-Li
  • Tzeng, Gwo-Hshiung

Abstract

Shortest-path (minimum travel time) routing has been adopted over the past few decades. However, many studies have shown that a driver's route and the shortest path differ widely in significant ways, and that most drivers use several criteria in selecting their routes. Since route choice criteria have been the subject of controversy, this study develops an individual behavioral-based mechanism for exploring the crucial criteria affecting drivers' route-selection decisions. On the basis of the weight-assessing model and the habitual domain theory, this study presents the dynamic change of route choice criteria according to their dynamic weights. Furthermore, the effects of information on drivers' route-formulating behaviors are investigated as well in order to provide some valuable suggestions for implementing Advanced Traveler Information Systems (ATIS) in the future. An empirical study in Taipei City was conducted to show the feasibility and applicability of our proposed method and the empirical results indicate excellent performance in practice.

Suggested Citation

  • Chen, Ting-Yu & Chang, Hsin-Li & Tzeng, Gwo-Hshiung, 2001. "Using a weight-assessing model to identify route choice criteria and information effects," Transportation Research Part A: Policy and Practice, Elsevier, vol. 35(3), pages 197-224, March.
    • Handle: RePEc:eee:transa:v:35:y:2001:i:3:p:197-224
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    References listed on IDEAS

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    1. Caplice, Chris & Mahmassani, Hani S., 1992. "Aspects of commuting behavior: Preferred arrival time, use of information and switching propensity," Transportation Research Part A: Policy and Practice, Elsevier, vol. 26(5), pages 409-418, September.
    2. Tzeng, Gwo-Hshiung & Chen, Ting-Yu & Wang, Jih-Chang, 1998. "A weight-assessing method with habitual domains," European Journal of Operational Research, Elsevier, vol. 110(2), pages 342-367, October.
    3. Khattak, Asad J. & Schofer, Joseph L. & Koppelman, Frank S., 1993. "Commuters' enroute diversion and return decisions: Analysis and implications for advanced traveler information systems," Transportation Research Part A: Policy and Practice, Elsevier, vol. 27(2), pages 101-111, April.
    4. Yang, Hai & Kitamura, Ryuichi & Jovanis, Paul P. & Vaughn, Kenneth M. & Abdel-aty, Mohammed A. & Reddy, Prasuna Dvg, 1993. "Exploration Of Driver Route Choice With Advanced Traveler Information Using Neural Network Concepts," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt53d2t6df, Institute of Transportation Studies, UC Berkeley.
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    Cited by:

    1. Bittihn, Stefan & Schadschneider, Andreas, 2021. "The effect of modern traffic information on Braess’ paradox," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 571(C).
    2. Thomas, Tom & Tutert, Bas, 2015. "Route choice behavior in a radial structured urban network: Do people choose the orbital or the route through the city center?," Journal of Transport Geography, Elsevier, vol. 48(C), pages 85-95.
    3. Jaap Vreeswijk & Tom Thomas & Eric Berkum & Bart Arem, 2014. "Perception bias in route choice," Transportation, Springer, vol. 41(6), pages 1305-1321, November.
    4. Papinski, Dominik & Scott, Darren M., 2011. "A GIS-based toolkit for route choice analysis," Journal of Transport Geography, Elsevier, vol. 19(3), pages 434-442.
    5. Pegado-Bardayo, Ana & Lorenzo-Espejo, Antonio & Muñuzuri, Jesús & Aparicio-Ruiz, Pablo, 2023. "A data-driven decision support system for service completion prediction in last mile logistics," Transportation Research Part A: Policy and Practice, Elsevier, vol. 176(C).

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