AgBM-DTALite: An integrated modelling system of agent-based travel behaviour and transportation network dynamics

Chenfeng Xiong, Xuesong Zhou, Lei Zhang

Research output: Contribution to journalArticlepeer-review

21 Scopus citations


Advanced modelling methods and products, such as an integrated advanced travel demand model and a fine-grained time-sensitive network that can operate at statewide, metropolitan and subarea/corridor levels, are required by a number of transportation planning agencies to meet their objectives and address various key challenges. This research develops an application-ready integrated transportation model that can predict, in a future-year scenario or in a hypothetical scenario, both the changes in travel behavioural adjustments and the dynamics in traffic conditions. The integrated framework embeds theoretically sound behavioural foundation by incorporating agent-based searching, information acquisition, learning, knowledge updating and decision-making. Multidimensional travel behaviour, including mode choice, route choice, departure time choice and en-route diversion, is considered. Behavioural user equilibrium is defined without assuming perfect rationality. A dynamic traffic simulation engine is employed to model and simulate real-time traffic conditions. Data exchanges between the travel demand model and the traffic simulation are explained in detail. The integration is demonstrated using a real-world case study. Future applications should cover a wide spectrum of scenarios in transportation planning/policy and traffic operations/control analyses.

Original languageEnglish (US)
Pages (from-to)141-150
Number of pages10
JournalTravel Behaviour and Society
StatePublished - Jul 2018


  • Agent-based model
  • Integrated model
  • Learning
  • Traffic simulation
  • Transportation system
  • Travel behaviour

ASJC Scopus subject areas

  • Transportation


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