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Stochastic dynamic traffic assignment model under emergent incidents

Stochastic dynamic traffic assignment model under emergent incidents

Ji, Xun, Shao, Chunfu and Wang, Bobin ORCID: 0000-0003-4437-2490 (2016) Stochastic dynamic traffic assignment model under emergent incidents. Procedia Engineering, 137 (2016). pp. 620-629. ISSN 1877-7058 (doi:https://doi.org/10.1016/j.proeng.2016.01.299)

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Abstract

Urban emergent incidents affect transportation operation and result in the rapid spread of traffic congestion in network, so it’s necessary to analyze the dynamic changes of traffic flow distribution under emergent incidents. Therefore, model and algorithm for the dynamic traffic assignment problem under emergent incidents have been highly concerned by government and scholars. This paper proposes a stochastic dynamic traffic assignment (SDTA) model based user optimum considering the loss of node capacity and change of network structure under traffic and environment emergencies. The Nested Logit model is used to describe the departure time and path choice. Then, the variational inequality formulation is proposed and discrete dynamic network loading algorithm is designed and validated by a numerical example. The results show that the model and algorithm can be used to express the development trend of actual dynamic network under emergency.

Item Type: Article
Additional Information: © 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Uncontrolled Keywords: stochastic dynamic traffic assignment, user optimum, variational inequality formulation, emergent incidents
Faculty / Department / Research Group: Faculty of Business
Faculty of Business > Department of Systems Management & Strategy
Faculty of Business > Networks and Urban Systems Centre (NUSC)
Faculty of Business > Networks and Urban Systems Centre (NUSC) > Connected Cities Research Group
Last Modified: 01 Jul 2020 23:57
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
Selected for REF2021: None
URI: http://gala.gre.ac.uk/id/eprint/28208

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