AgBM-DTALite: An integrated modelling system of agent-based travel behaviour and transportation network dynamics. (July 2018)

• Record Type: Journal Article
• Authors: Xiong, Chenfeng; Zhou, Xuesong; Zhang, Lei
• Is Part Of: Travel behaviour and society. Volume 12(2018)
• Page Start: 141
• Page End: 150
• Publication Date: 2018-07
• Subjects: Integrated model -- Transportation system -- Travel behaviour -- Traffic simulation -- Learning -- Agent-based model; Transportation -- Periodicals; Population geography -- Periodicals; 303.48305
• DOI: 10.1016/j.tbs.2017.04.004
• Languages: English
• ISSNs: 2214-367X

Abstract

Highlights: An integrated modeling system of travel behavior and dynamic traffic simulation. Agent-based behavior with search, information acquisition, learning and knowledge. Multidimensional behavior of travel mode, route, and departure time choices. Behavioral User Equilibrium (BUE) without assuming perfect rationality. The BUE and agent-based models improve the convergence property for applications. Abstract: 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.