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

Authored by Lei Zhang, Chenfeng Xiong, Xuesong Zhou

Date Published: 2018

DOI: 10.1016/j.tbs.2017.04.004

Sponsors: United States Department of Energy (DOE) United States National Science Foundation (NSF)

Platforms: No platforms listed

Model Documentation: Other Narrative Flow charts

Model Code URLs: Model code not found

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. (C) 2017 Hong Kong Society for Transportation Studies. Published by Elsevier Ltd. All rights reserved.

Tags

Agent-based model Learning Integrated model travel behaviour Choice Transportation system Traffic simulation