交通运输系统工程与信息

交通运输系统工程与信息 ›› 2011, Vol. 11 ›› Issue (3): 138-143.

• 系统工程理论与方法 • 上一篇    下一篇

紧急疏散条件下交通控制设施选址研究

张雄飞1，史其信*1，HE Rachel2，BAN Jeff3   

  1. 1. 清华大学 交通研究所，北京 100084; 2. 美国圣克拉拉大学 土木工程系，加州 95053； 3. 美国伦斯勒理工学院 土木与环境工程系，纽约州 12180
  • 收稿日期:2011-04-08 修回日期:2011-05-12 出版日期:2011-06-25 发布日期:2011-07-18
  • 作者简介:张雄飞（1984-），女，湖北省武汉市人，博士生

Finding Critical Traffic Control Locations for Emergency Evacuation

ZHANG Xiong-fei1, SHI Qi-xin1, HE Rachel2, BAN Jeff3   

  1. 1. Institute of Traffic Engineering, Tsinghua University, Beijing 100084, China; 2. Department of Civil Engineering, Santa Clara University, Santa Clara, CA 95053, USA; 3. Department of Civil and Environmental Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
  • Received:2011-04-08 Revised:2011-05-12 Online:2011-06-25 Published:2011-07-18

摘要： 针对地震、飓风等自然或者人为灾害条件下，大规模路网疏散的交通管理问题进行了研究. 由于紧急疏散条件下，交通控制设施和管理人员有限，很难覆盖交通网络中的所有交通节点，因此，识别出路网中重要的交通节点并加以控制，以达到最优的系统状态显得尤为重要. 为了解决这一迫切的既理论而又实际的问题，本研究提出了一个混合整数非线性规划（Mixed Integer NonLinear Programming,MINLP）模型，以确定最优的交通控制节点选址，并同时确定这些控制节点的最佳交通控制策略，以最大限度地减少整个交通系统成本. 数值算例表明了模型的适用性和合理性，同时得出结论，只要识别出重要的交通节点并加以控制，系统也能接近所有交通节点都控制时的理想最优状态.

关键词: 交通工程, 用户均衡, 系统最优, 非线性规划, 紧急疏散

Abstract: This paper aims to explore mass evacuation problem of large-scale road network in the event of natural or man-made disasters, such as earthquakes and hurricanes. Under emergency evacuation, traffic control devices and personnel are limited. It is impossible to control all the traffic nodes on the network. Therefore, it is crucial to find the most critical traffic control locations or intersections to deploy control devices or arrange manual evacuation guidance, so that the best system performance can be achieved. In order to answer the urgent theoretical and practical deployment problems, this study proposes a mixed integer nonlinear programming (MINLP) model, which aims to identify the most crucial intersections, and simultaneously, the optimal traffic control strategies at those locations, to minimize the total system costs. Numerical example shows that the model performs reasonably well, and with the appropriate set of critical intersections controlled, the resulting system performance does approach the system optimal state with all intersections under control.

Key words: traffic engineering, user equilibrium, system optimal, nonlinear programming, emergency evacuation

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