Personalized real-time traffic information provision: Agent-based optimization model and solution framework

Jiaqi Ma, Brian L. Smith, Xuesong Zhou

Research output: Contribution to journalArticlepeer-review

45 Scopus citations


The advancement of information and communication technology allows the use of more sophisticated information provision strategies for real-time congested traffic management in a congested network. This paper proposes an agent-based optimization modeling framework to provide personalized traffic information for heterogeneous travelers. Based on a space-time network, a time-dependent link flow based integer programming model is first formulated to optimize various information strategies, including elements of where and when to provide the information, to whom the information is given, and what alternative route information should be suggested. The analytical model can be solved efficiently using off-the-shelf commercial solvers for small-scale network. A Lagrangian Relaxation-based heuristic solution approach is developed for medium to large networks via the use of a mesoscopic dynamic traffic simulator.

Original languageEnglish (US)
Pages (from-to)164-182
Number of pages19
JournalTransportation Research Part C: Emerging Technologies
StatePublished - Mar 1 2016


  • Agent-based modeling
  • Dynamic traffic management
  • Network modeling
  • Traveler information provision

ASJC Scopus subject areas

  • Computer Science Applications
  • Management Science and Operations Research
  • Automotive Engineering
  • Transportation


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