基于蚁群算法的人车混合疏散优化及混合比例分析

为解决紧急情况下的人车混合疏散问题, 以人车混合疏散的总时间最短、混合道路利用程度最高为目标,建立了一种人车混合疏散的多目标优化模型, 针对该模型设计了多目标蚁群优化算法及其改进算法,并应用于大型体育场及其周边路网集成环境中进行了仿真实验, 分析了不同人车混合比例下的疏散性能,结果表明:该模型及算法对人车混合交通流疏散问题具有良好的效果, 尤其是当行人所占比例为 50%-80% 时,人车混合疏散效果在两个目标上较优.该模型和算法有助于为大型公共场所人车混合安全疏散预案的制定提供决策支持.

Optimization and proportion analysis of pedestrian-vehicle mixed evacuation based on ant colony algorithm

Based on the analysis of characteristics of traffic flow mixed pedestrians and vehicles,a multiobjective optimization model was proposed to tackle pedestrian-vehicle mixed evacuation problem under emergency situation.To solve this model,a modified multi-objective ant colony optimization algorithm was designed to minimize total evacuation time and maximize mixed road utilization of the whole road network simultaneously.The proposed model and algorithm were tested using a case integrated a stadium of Wuhan Sports Center(in China) and road network around it.Evacuation performances with different mixed proportion of pedestrians to vehicles were analyzed.The experimental results show that this model and algorithm are effective for evacuation problem with mixed traffic flow,and mixed evacuation results are better when the mixed ratio of pedestrian ranges from 50%to 80%,which offers decision support for planning pedestrian-vehicle mixed evacuation in large common place.