Interactive travel choices and traffic forecast in a doubly dynamical system with user inertia and information provision

Wei Liu, Xinwei Li, Fangni Zhang, Hai Yang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

54 Citations (Scopus)


This study models the joint evolution (over calendar time) of travelers’ departure time and mode choices, and the resulting traffic dynamics in a bi-modal transportation system. Specifically, we consider that, when adjusting their departure time and mode choices, travelers can learn from their past travel experiences as well as the traffic forecasts offered by the smart transport information provider/agency. At the same time, the transport agency can learn from historical data in updating traffic forecast from day to day. In other words, this study explicitly models and analyzes the dynamic interactions between transport users and traffic information provider. Besides, the impact of user inertia is taken into account in modeling the traffic dynamics. When exploring the convergence of the proposed model to the dynamic bi-modal commuting equilibrium, we find that appropriate traffic forecast can help the system converge to the user equilibrium. It is also found that user inertia might slow down the convergence speed of the day-to-day evolution model. Extensive sensitivity analysis is conducted to account for the impacts of inaccurate parameters adopted by the transport agency.

Original languageEnglish
Pages (from-to)711-731
Number of pages21
JournalTransportation Research Part C: Emerging Technologies
Publication statusPublished - Dec 2017
Externally publishedYes


  • Bi-modal
  • Bottleneck model
  • Day-to-day dynamics
  • Traffic forecast
  • User inertia

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Automotive Engineering
  • Transportation
  • Computer Science Applications


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