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Travel time prediction and departure time adjustment behavior dynamics in a congested traffic system

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  • Chang, Gang-Len
  • Mahmassani, Hani S.

Abstract

This paper examines two heuristic rules proposed for describing urban commuters' predictions of travel time as well as the adjustments of departure time in response to unacceptable arrivals in their daily commute under limited information. It is based on the notion that the magnitude of the predicted travel time depends on each commuter's own experience, including recallable travel time, schedule delay, and difficulties in searching for a satisfactory departure time. An explanatory analysis is first performed to compare these two rules, based on the information provided by a set of commuters interacting over 24 days through a simulated traffic system. A more elaborate model specification which captures the dynamic interrelation between the commuter's cumulative and recent experience with the traffic system's performance is then proposed. The model parameters are estimated with explicit consideration of the serial correlation arising from repeated decisions by the same individuals and the contemporaneous interaction with other system users' decisions through the traffic system's performance.

Suggested Citation

  • Chang, Gang-Len & Mahmassani, Hani S., 1988. "Travel time prediction and departure time adjustment behavior dynamics in a congested traffic system," Transportation Research Part B: Methodological, Elsevier, vol. 22(3), pages 217-232, June.
    • Handle: RePEc:eee:transb:v:22:y:1988:i:3:p:217-232
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    Cited by:

    1. Hu, Zejing & Smirnova, M.N. & Zhang, Yongliang & Smirnov, N.N. & Zhu, Zuojin, 2021. "Estimation of travel time through a composite ring road by a viscoelastic traffic flow model," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 181(C), pages 501-521.
    2. Erel Avineri & Joseph Prashker, 2006. "The Impact of Travel Time Information on Travelers’ Learning under Uncertainty," Transportation, Springer, vol. 33(4), pages 393-408, July.
    3. Víctor Cantillo & Juan de Dios Ortúzar & Huw C. W. L. Williams, 2007. "Modeling Discrete Choices in the Presence of Inertia and Serial Correlation," Transportation Science, INFORMS, vol. 41(2), pages 195-205, May.
    4. Kitamura, Ryuichi, 1990. "Panel Analysis in Transportation Planning: An Overview," University of California Transportation Center, Working Papers qt86v0f7zh, University of California Transportation Center.
    5. G. E. Cantarella & D. P. Watling, 2016. "Modelling road traffic assignment as a day-to-day dynamic, deterministic process: a unified approach to discrete- and continuous-time models," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 5(1), pages 69-98, March.
    6. Maurizio Faccio & Serena Finco & Ilenia Zennaro, 2021. "Sustainable People Home-Work Logistics: An Integrated Model of Circular Economy in the Chiampo Valley," Sustainability, MDPI, vol. 13(21), pages 1-13, October.
    7. Muhammad Irawan & Tomonori Sumi, 2011. "Modeling Departure Time of Students on Public Transports in Yogyakarta, Indonesia," Modern Applied Science, Canadian Center of Science and Education, vol. 5(4), pages 1-3, August.
    8. David Watling & Giulio Cantarella, 2015. "Model Representation & Decision-Making in an Ever-Changing World: The Role of Stochastic Process Models of Transportation Systems," Networks and Spatial Economics, Springer, vol. 15(3), pages 843-882, September.
    9. Li, Zhengming & Smirnova, M.N. & Zhang, Yongliang & Smirnov, N.N. & Zhu, Zuojin, 2022. "Tunnel speed limit effects on traffic flow explored with a three lane model," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 194(C), pages 185-197.
    10. Richard H. M. Emmerink & Paul van Beek, 1997. "Empirical Analysis of Work Schedule Flexibility: Implications for Road Pricing and Driver Information Systems," Urban Studies, Urban Studies Journal Limited, vol. 34(2), pages 217-234, February.
    11. Lo, H. & Hickman, M. & Walstad, M., 1996. "An Evaluation Taxonomy For Congestion Pricing," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt80g5s1km, Institute of Transportation Studies, UC Berkeley.
    12. Wang, Bobin & Shao, Chunfu & Li, Juan & Weng, Jinxian & Ji, Xun, 2015. "Holiday travel behavior analysis and empirical study under integrated multimodal travel information service," Transport Policy, Elsevier, vol. 39(C), pages 21-36.
    13. Lo, Hong K. & Lin, Wei-hua & Liao, Lawrence C. & Chang, Elbert & Tsao, Jacob, 1996. "A Comparison Of Traffic Models: Part 1, Framework," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt8608t44v, Institute of Transportation Studies, UC Berkeley.
    14. Hamed Alibabai & Hani S. Mahmassani, 2016. "Foxes and sheep: effect of predictive logic in day-to-day dynamics of route choice behavior," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 5(1), pages 53-67, March.
    15. Tang, Yue & Gao, Song & Ben-Elia, Eran, 2017. "An exploratory study of instance-based learning for route choice with random travel times," Journal of choice modelling, Elsevier, vol. 24(C), pages 22-35.
    16. Small, K. & Noland, R. & Koskenoja, P., 1995. "Socio-economic Attributes And Impacts Of Travel Reliability: A Stated Preference Approach," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt82n2w53k, Institute of Transportation Studies, UC Berkeley.
    17. Wong, Jinn-Tsai, 1997. "Basic concepts for a system for advance booking for highway use," Transport Policy, Elsevier, vol. 4(2), pages 109-114, April.

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