TY - JOUR
T1 - AgBM-DTALite
T2 - An integrated modelling system of agent-based travel behaviour and transportation network dynamics
AU - Xiong, Chenfeng
AU - Zhou, Xuesong
AU - Zhang, Lei
N1 - Publisher Copyright:
© 2017 Hong Kong Society for Transportation Studies
PY - 2018/7
Y1 - 2018/7
N2 - 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.
AB - 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.
KW - Agent-based model
KW - Integrated model
KW - Learning
KW - Traffic simulation
KW - Transportation system
KW - Travel behaviour
UR - http://www.scopus.com/inward/record.url?scp=85017424213&partnerID=8YFLogxK
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U2 - 10.1016/j.tbs.2017.04.004
DO - 10.1016/j.tbs.2017.04.004
M3 - Article
AN - SCOPUS:85017424213
SN - 2214-367X
VL - 12
SP - 141
EP - 150
JO - Travel Behaviour and Society
JF - Travel Behaviour and Society
ER -