基于双层网络优化模型的紧急状况下交通疏散管理

Traffic management strategies for emergency evacuation based on a bi-level network optimization model

现有文献缺乏先进的模型来确定疏散网络中用于实施不间断流量和信号控制策略的最优交叉口集,而最后交叉口集能够产生最大的疏散运行效率和最大利用有限成本。本文针对这种情况提出的网络优化模型,有助于解决在应急规划过程中长期困扰交通主管部门的诸多关键问题:(1)采用信号和断流措施的交叉口的数量;(2)交叉口集的最优位置的确定;(3)对于不间断交通的交叉口,如何合理设计转弯限制方案。本文提出的模型包括双层框架。上层通过最小化总疏散时间来确定不间断交通和信号交叉口的最佳位置以及相应的转弯限制计划;下层则根据SUE原则来处理疏散交通的路线分配。该模型采用基于遗传算法的启发式算法求解。在不同疏散需求和预算水平下的广泛分析表明,不间断交通和信号交叉口的位置选择在应急交通的疏散管理中起着关键作用。通过显著减少总疏散时间(高达39%),模型在将有限资源优先分配到最合适的控制点方面大大优于现有实践。

The problem to be addressed in this paper is the lack of an advanced model in the literature to determine the optimal set of intersections in the evacuation network for implementing uninterrupted flow and signal control strategies,respectively,which can yield the maximum evacuation operational efficiency and the best use of available budgets.An network optimization model,proposed in response to such needs,contributes to addressing the following critical questions that have long challenged transportation authorities during emergency planning,namely given the topology of an evacuation network,evacuation demand distribution,and a limited budget,(1)how many intersections should be implemented with the signals and interrupted flow strategies,respectively;(2)what their most appropriate locations are;and(3)how turning restriction plans should be properly designed for those uninterrupted flow intersections.The proposed model features a bi-level framework.The upper level determines the best locations for uninterrupted flow and signalizes intersections as well as the corresponding turning restriction plans by minimizing the total evacuation time,while the lower level handles routing assignments of evacuation traffic based on the SUE principle.The proposed model is solved by a GA-based heuristic.Extensive analyses under various evacuation demand and budget levels have indicated that the location selection of uninterrupted flow and signalized intersections plays a key role in emergency traffic evacuation management.The proposed model substantially outperforms existing practices in prioritizing limited resources to the most appropriate control points by significantly reducing the total evacuation time(up to 39%).