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Article: Interactive travel choices and traffic forecast in a doubly dynamical system with user inertia and information provision

TitleInteractive travel choices and traffic forecast in a doubly dynamical system with user inertia and information provision
Authors
KeywordsTraffic forecast
Bi-modal
Bottleneck model
Day-to-day dynamics
User inertia
Issue Date2017
Citation
Transportation Research Part C: Emerging Technologies, 2017, v. 85, p. 711-731 How to Cite?
Abstract© 2017 Elsevier Ltd This study models the joint evolution (over calendar time) of travelers’ departure time and mode choices, and the resulting traffic dynamics in a bi-modal transportation system. Specifically, we consider that, when adjusting their departure time and mode choices, travelers can learn from their past travel experiences as well as the traffic forecasts offered by the smart transport information provider/agency. At the same time, the transport agency can learn from historical data in updating traffic forecast from day to day. In other words, this study explicitly models and analyzes the dynamic interactions between transport users and traffic information provider. Besides, the impact of user inertia is taken into account in modeling the traffic dynamics. When exploring the convergence of the proposed model to the dynamic bi-modal commuting equilibrium, we find that appropriate traffic forecast can help the system converge to the user equilibrium. It is also found that user inertia might slow down the convergence speed of the day-to-day evolution model. Extensive sensitivity analysis is conducted to account for the impacts of inaccurate parameters adopted by the transport agency.
Persistent Identifierhttp://hdl.handle.net/10722/281475
ISSN
2019 Impact Factor: 6.077
2015 SCImago Journal Rankings: 2.062
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorLiu, Wei-
dc.contributor.authorLi, Xinwei-
dc.contributor.authorZhang, Fangni-
dc.contributor.authorYang, Hai-
dc.date.accessioned2020-03-13T10:37:57Z-
dc.date.available2020-03-13T10:37:57Z-
dc.date.issued2017-
dc.identifier.citationTransportation Research Part C: Emerging Technologies, 2017, v. 85, p. 711-731-
dc.identifier.issn0968-090X-
dc.identifier.urihttp://hdl.handle.net/10722/281475-
dc.description.abstract© 2017 Elsevier Ltd This study models the joint evolution (over calendar time) of travelers’ departure time and mode choices, and the resulting traffic dynamics in a bi-modal transportation system. Specifically, we consider that, when adjusting their departure time and mode choices, travelers can learn from their past travel experiences as well as the traffic forecasts offered by the smart transport information provider/agency. At the same time, the transport agency can learn from historical data in updating traffic forecast from day to day. In other words, this study explicitly models and analyzes the dynamic interactions between transport users and traffic information provider. Besides, the impact of user inertia is taken into account in modeling the traffic dynamics. When exploring the convergence of the proposed model to the dynamic bi-modal commuting equilibrium, we find that appropriate traffic forecast can help the system converge to the user equilibrium. It is also found that user inertia might slow down the convergence speed of the day-to-day evolution model. Extensive sensitivity analysis is conducted to account for the impacts of inaccurate parameters adopted by the transport agency.-
dc.languageeng-
dc.relation.ispartofTransportation Research Part C: Emerging Technologies-
dc.subjectTraffic forecast-
dc.subjectBi-modal-
dc.subjectBottleneck model-
dc.subjectDay-to-day dynamics-
dc.subjectUser inertia-
dc.titleInteractive travel choices and traffic forecast in a doubly dynamical system with user inertia and information provision-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.trc.2017.10.021-
dc.identifier.scopuseid_2-s2.0-85033706858-
dc.identifier.volume85-
dc.identifier.spage711-
dc.identifier.epage731-
dc.identifier.isiWOS:000423006600037-
dc.identifier.issnl0968-090X-

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