Please use this identifier to cite or link to this item: https://doi.org/10.1155/2018/2524838
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dc.titleFlexible Emergency Vehicle Network Design considering Stochastic Demands and Inverse-Direction Lanes
dc.contributor.authorWang, H.
dc.contributor.authorXiao, L.
dc.contributor.authorChen, Z.
dc.date.accessioned2021-12-09T05:04:39Z
dc.date.available2021-12-09T05:04:39Z
dc.date.issued2018
dc.identifier.citationWang, H., Xiao, L., Chen, Z. (2018). Flexible Emergency Vehicle Network Design considering Stochastic Demands and Inverse-Direction Lanes. Journal of Advanced Transportation 2018 : 2524838. ScholarBank@NUS Repository. https://doi.org/10.1155/2018/2524838
dc.identifier.issn0197-6729
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/210133
dc.description.abstractWe study transportation network design with stochastic demands and emergency vehicle (EV) lanes. Different from previous studies, this paper considers two groups of users, auto and EV travelers, whose road access rights are differentiated in the network, and addresses the value of incorporating inverse-direction lanes in network design. We formulate the problem as a bilevel optimization model, where the upper-level model aims to determine the optimal design of EV lanes and the lower-level model uses the user equilibrium principle to forecast the route choice of road users. A simulation-based genetic algorithm is proposed to solve the model. With numerical experiments, we demonstrate the value of deploying inverse-direction EV lanes and the computational efficiency of the proposed algorithm. We reach an intriguing finding that both regular and EV lane users can benefit from building EV lanes. © 2018 Hua Wang et al.
dc.publisherHindawi Limited
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.sourceScopus OA2018
dc.typeArticle
dc.contributor.departmentCIVIL AND ENVIRONMENTAL ENGINEERING
dc.description.doi10.1155/2018/2524838
dc.description.sourcetitleJournal of Advanced Transportation
dc.description.volume2018
dc.description.page2524838
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