应急车辆动态路径选择的两阶段优化模型

为研究突发事件情境下交通路网动态变化时的应急车辆路径选择问题，提出应急车辆动态路径选择的两阶段调度优化模型。通过结合路网动态状况和应急救援特征，建立基于最大路径可靠度和最短行程时间的两阶段优化模型；通过混沌搜索改进布谷鸟算法初始种群，并加入蛙跳算法改进局部搜索操作，设计混合布谷鸟算法，改善全局寻优能力；以某市某区部分区域路网为例，将该区域路网实时交通数据应用于模型和求解算法中。实验表明，利用两阶段优化模型和算法编码方案能成功获得出发点到救援点的动态可靠路径，相同行驶路径情况下模型与算法求解的最短行程时间与实地驾车获得的最短行程时间最大误差不超过8%，说明优化模型可行。3 种不同算法求解K最短路径的结果发现，混合布谷鸟算法得到的最短行程时间比粒子群算法和 经典布谷鸟算法得到的结果都要小，且计算时间最短，表明混合布谷鸟算法求解的结果最优，性能最好。

Emergency Vehicle Dynamic Path Selection Based on Two-stage Objective Optimization Model

This paper investigates the emergency vehicle routing problem when the traffic network changes dynamically and proposes a two-stage scheduling optimization model for emergency vehicle routing. The two-stage optimization model with the maximum path reliability and the shortest travel time is developed by combining the dynamic situation of the road network and the characteristics of emergency rescue. The initial population of Cuckoo algorithm is improved by chaotic search and the frog leaping algorithm is added to improve the local search operation. The hybrid Cuckoo algorithm is designed to improve the global optimization ability. Taking a regional road network in a city as an example, the real-time traffic data of the regional road network and the data obtained from field driving are applied to the model and solution algorithm. The results show that the dynamic reliable path from the starting point to the rescue point can be successfully obtained using the proposed model and algorithm. For the same driving path, the maximum error of the shortest travel time solved by the proposed method and the shortest travel time obtained by field driving is no more than 8%, indicating good feasibility of the method. Compared to the particle swarm optimization algorithm and the classical Cuckoo algorithm to solve the K shortest path, the hybrid Cuckoo algorithm can obtain the shortest travel time with fastest calculation speed, which shows that the hybrid Cuckoo algorithm has the best solution and performance.