关键词: 高铁站, 拥挤度, 应急疏散, 多目标优化, 量子蚁群算法

Abstract:

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 integrating transportation， transfer and service. In the event of an emergency， it is difficult to evacuate the huge passenger 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 station， 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 improvement 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.

Key words: high-speed railway station, emergency evacuation, crowding degree, multi-objective optimization, quantum ant colony algorithm