突发事件下高铁站应急疏散多目标优化模型与自适应量子蚁群算法

Multi-objective Optimization Model and Adaptive Quantum Ant Colony Algorithm for Emergency Evacuation of High-speed Railway Stations under Emergencies

突发事件下公共场所人员的应急疏散问题是目前国内外研究的热点,其中高铁站应急情境下的人员疏散及路径优化也随着高铁的快速发展逐渐引起了人们的重视。本文考虑拥挤度会对疏散人员心理行为及疏散效率产生影响,以高铁站人员应急疏散过程中的拥挤度与总疏散时间为目标,建立了高铁站应急疏散路径多目标优化数学模型,设计了一种改进的自适应量子蚁群算法进行求解,并与常规算法求解结果做了对比分析。通过算例进行模拟实验,结果表明,所提出的模型较好地兼顾了疏散路径的安全性与时效性,且设计的算法具有良好的全局性和收敛性,有助于进一步完善我国高速铁路客运运作管理体系。

The high-speed rail station,as a connection point for traffic inside and outside the city,has greatly promoted the economic development of the surrounding areas.It is a comprehensive transportation hub integrat⁃ing transportation,transfer and service.In the event of an emergency,it is difficult to evacuate the huge pas⁃senger flow in a short period of time,and it is easy to cause casualties.Therefore,it is necessary to study how to organize efficient and orderly emergency evacuation activities in a limited time,optimize the evacuation path of people in high-speed railway stations,and reduce casualties and property losses caused by emergencies.The first part of this paper reviews the relevant literature on the evacuation process under emergencies,and finds that there are still some shortcomings in the existing research.First,in terms of model construction,the existing research usually adopts the general evacuation model of large public places,which does not reflect the characteristics of the evacuation problem of high-speed rail.Secondly,in the method of solving the objective problem,some existing algorithms are difficult to obtain effective and stable results when solving nonlinear programming models with multiple optimization objectives and multiple constraints.The second part introduces the characteristics of the evacuation problem of high-speed railway stations and the characteristic factors that need to be considered when modeling.According to the structural characteristics of the high-speed railway sta⁃tion,a multi-objective optimization model for the evacuation path of people in the high-speed railway station is established.The optimization goal of the model is to reduce the total evacuation time of all evacuees’as much as possible,balance the load of the entire evacuation network,and calculate a feasible evacuation path that can evacuate people on time under the condition of ensuring safety.In the third part,an improved quantum ant colony algorithm is designed based on the methods of increasing the quantum revolving gate adaptive improve⁃ment mechanism,increasing the individual mutation strategy,and improving the pheromone update method.And through the numerical example comparison experiment,it is verified that the improved quantum ant colony algorithm can effectively overcome the shortcomings of the traditional ant colony algorithm that the convergence speed is slow and it is easy to fall into the local optimum.In the fourth part,based on the actual survey data of a high-speed railway station,an experimental case of the evacuation problem of high-speed railway station is constructed with different scales of evacuating personnel,and the optimization results of the improved quantum ants are compared and tested,which shows the effectiveness and efficiency of the model.In summary,the emergency evacuation of high-speed railway stations is studied under the emergency situation,the relationship between evacuation efficiency and personnel density and congestion is analyzed,and finally a multi-objective evacuation path optimization model is established.In order to enhance the efficiency of model solving,an improved quantum ant colony algorithm is designed based on a mixed strategy,which overcomes the defect that the ant colony algorithm is prone to fall into prematurity.The optimization results of the algorithm can provide a more scientific and effective decision-making basis for the path selection of emergency evacuation of high-speed railway stations.