Emergency response facility location in transportation networks: A literature review

被引:40
作者
Liu, Yang [1 ]
Yuan, Yun [2 ,3 ]
Shen, Jieyi [1 ]
Gao, Wei [1 ]
机构
[1] Nanjing Agr Univ, Coll Artificial Intelligence, Nanjing 210031, Peoples R China
[2] Dalian Maritime Univ, Coll Transportat Engn, Dalian 116026, Peoples R China
[3] Univ Utah, Coll Engn, Salt Lake City, UT 84112 USA
基金
中国博士后科学基金; 美国国家科学基金会;
关键词
Transportation engineering; Emergency facility location; Transportation networks; Travel time; Machine learning; TRAVEL-TIME ESTIMATION; CELL TRANSMISSION MODEL; VEHICLE-ROUTING PROBLEM; OPTIMIZATION MODEL; AMBULANCE LOCATION; SHELTER LOCATION; MATHEMATICAL PROGRAMS; GENETIC ALGORITHM; MEDICAL FACILITY; COVERING MODELS;
D O I
10.1016/j.jtte.2021.03.001
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Emergency response activity relies on transportation networks. Emergency facility location interacts with transportation networks clearly. This review is aimed to provide a combined framework for emergency facility location in transportation networks. The article reveals emergency response activities research clusters, issues, and objectives according to keywords co-occurrence analysis. Four classes of spatial separation models in transportation networks, including distance, routing, accessibility, and travel time are introduced. The stochastic and time-dependent characteristics of travel time are described. Travel time estimation and prediction method, travel time under emergency vehicle preemption, transportation network equilibrium method, and travel time in degradable networks are demonstrated. The emergency facilities location models interact with transportation networks, involving location-routing model, location models embedded with accessibility, location models embedded with travel time, and location models employing mathematical program with equilibrium constraints are reviewed. We then point out the-state-of-art challenges: ilities-oriented, evolution landscape and sequential decision modelling, data driven optimization approach, and machine learning-based algorithms. (C) 2021 Periodical Offices of Chang'an University. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd.
引用
收藏
页码:153 / 169
页数:17
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