A dynamic evacuation model for pedestrian-vehicle mixed-flow networks

被引:30
|
作者
Zhang, Xin [1 ]
Chang, Gang-len [1 ]
机构
[1] Univ Maryland, Dept Civil & Environm Engn, College Pk, MD 20742 USA
关键词
Mixed flow; Signal optimization; Evacuation; Integer-linear programming; Pedestrian; MICRO-SIMULATION; SYSTEM; OPTIMIZATION; CAPACITY; BEHAVIOR; TIME;
D O I
10.1016/j.trc.2014.01.003
中图分类号
U [交通运输];
学科分类号
08 ; 0823 ;
摘要
In urban emergency evacuation, a potentially large number of evacuees may depend either on transit or other modes, or need to walk a long distance, to access their passenger cars. In the process of approaching the designated pick-up points or parking areas for evacuation, the massive number of pedestrians may cause tremendous burden to vehicles in the roadway network. Responsible agencies often need to contend with congestion incurred by massive vehicles emanating from parking garages, evacuation buses generated from bus stops, and the conflicts between evacuees and vehicles at intersections. Hence, an effective plan for such evacuation needs to concurrently address both the multi-modal traffic route assignment and the optimization of network signal controls for mixed traffic flows. This paper presents an integrated model to produce the optimal distribution of vehicle and pedestrian flows, and the responsive network signal plan for massive mixed pedestrian-vehicle flows within the evacuation zone. The proposed model features its effectiveness in accounting for multiple types of evacuation vehicles, the interdependent relations between pedestrian and vehicle flows via some conversion locations, and the inevitable conflicts between intersection turning vehicle and pedestrian flows. An illustrating example concerning an evacuation around the M&T stadium area has been presented, and the results indicate the promising properties of our proposed model, especially on reflecting the complex interactions between vehicle and pedestrian flows and the favorable use of high-occupancy vehicles for evacuation operations. (C) 2014 Elsevier Ltd. All rights reserved.
引用
收藏
页码:75 / 92
页数:18
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