标题:An Integrated Control Model for Freeway Corridor Under Nonrecurrent Congestion
作者:Liu, Yue; Chang, Gang-Len; Yu, Jie
作者机构:[Liu, Yue] Univ Wisconsin, Milwaukee, WI 53201 USA.; [Chang, Gang-Len] Univ Maryland, College Pk, MD 20742 USA.; [Yu, Jie] Shandong Univ, Sch Cont 更多
通讯作者:Liu, Y.
通讯作者地址:[Liu, Y]Univ Wisconsin, Milwaukee, WI 53201 USA.
来源:IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
出版年:2011
卷:60
期:4
页码:1404-1418
DOI:10.1109/TVT.2011.2115264
关键词:Diversion; freeway operation; integrated traffic control; traffic flow; model
摘要:This paper presents an integrated model and its solution algorithm for freeway corridor control during incident management. With a parallel arterial as the detour route, the proposed model aims at producing the optimal diversion rates from the freeway mainline to relieve the congestion at the incident segment and concurrently adjust signal timings at the arterial intersections to best accommodate the detour traffic. Different from previous studies, the presented model and algorithm have the following two critical features: 1) modeling explicitly the evolution of detour traffic along the ramps and surface streets with a set of dynamic network flow formulations to capture the local bottlenecks caused by demand surge due to diversion operations and to properly set the responsive signal timing plans and 2) developing a multiobjective optimization framework to maximize the utilization of the available corridor capacity via detour operations but not to incur excessive congestion on the arterials and ramps. This study employs a genetic algorithm (GA)-based heuristic to efficiently yield the reliable solution, depending on the decision maker's preference. Extensive numerical tests on a segment along the I-95 corridor with its neighboring arterials have demonstrated the potential of the developed model for integrated freeway corridor control.
收录类别:EI;SCOPUS;SCIE;SSCI
WOS核心被引频次:18
Scopus被引频次:22
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-79955976818&doi=10.1109%2fTVT.2011.2115264&partnerID=40&md5=507dfa8582503f177f3db043f1a1c08c
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