【作者】 王泽；

【导师】 安实；

【作者基本信息】 哈尔滨工业大学 ， 交通运输规划及管理， 2010， 硕士

【摘要】 台风是夏秋季节我国东南沿海各省频繁遭遇的主要自然灾害,造成了巨大的人员伤亡和财产损失。在台风来临之前做好应急疏散方案为应急管理者提供决策支持具有重要意义。由于中国现阶段经济和道路交通发展的国情,城市规模、人口密度远大于发达国家,家庭小汽车普及率较低,一旦发生台风灾害事件,政府需要协调组织大容量公共运输工具为主要交通方式,实施较大规模和数量的强制性人口疏散,进而高效利用城市路网通行能力完成紧急疏散。因此,本文针对台风灾害情形下,如何进行公交系统的疏散集结点的选址和疏散车辆路径规划进行研究,以期提高方案的疏散效率,最大限度的减少人员伤亡和财产损失。为准确刻画台风灾害疏散情形,本文在系统分析了台风灾害特点、公交系统疏散管理以及需求特性的基础上,提出疏散响应时间限制的覆盖度定义及时空扩展疏散网络构造方法。通过对公交系统集结点选址决策和疏散车辆路径规划决策相关性进行分析,构建了选址-路径双层规划模型。选址-路径双层规划模型,从疏散的快速性、全面性两个角度,为保证疏散方案的高效性提供重要的模型支持。上层决策考虑如何制定疏散公共车辆的最佳疏散路线,使受灾人群尽可能快速的到达安全区域,给定若干疏散公交集结点的位置和疏散需求,通过求解车辆路径问题(Vehicle Routing Problems, VRP),达到疏散总时间(也包括等待时间)最小;下层决策考虑如何选择最优的集结点位置和人员安排,使得尽可能多的受灾人口得以安全疏散,根据疏散小区的需求和备选的集结点情形,通过求解最大覆盖选址问题(Maximum Covering Location Problems, MCLP),使得疏散总的需求覆盖度最大。鉴于所建双层规划模型的复杂性,约束较多且具有NP-hard (Non deterministic Polynomial)问题特征,采用遗传算法进行求解,通过设计合理的染色体编码方式,经过选择、交叉、变异等过程较快速的求解得到最佳的规划策略。更多还原

【Abstract】 Typhoon is the main natural disaster which the southeastern provinces of China often suffer in the summer and autumn seasons and that has brought great loss of property and casualties. So it is very important to prepare the emergency plan before the fall of typhoon for the emergency managers to make right decisions. Considering that China’s current economic situation& road transportation development are slower, the urban size& population density are much larger than that in the developed countries and that the popularity of family cars is in the low level, the government has to, if the typhoon occurs, organize the public transportation with large capacity to perform large number and scale of obligatory population evacuation for the purpose of accomplishing the emergency evacuation by efficient use of the city’s road network traffic capacity. Under the above situations, this essay analyses how to pick up the evacuation assembly stations and how to plan the evacuating route for the public vehicles under the descent of typhoon, so that the evacuation efficiency can be improved and the casualties& loss of property can also be reduced to the lowest level.In order to depict the real evacuation situation when the typhoon occurs, the essay comes up with the definition of coverage percent of the evacuation response time and the technique of network modeling for the time-space expansion, which based on the systematic analysis of the characteristics of the typhoon and those of the evacuation management& demand of public transportation. The essay is trying to analyze the correlation between the station selection of public transportation assembly and the decision of route planning of evacuating vehicles and also build model of the bi-level planning of station selection and passing route.As stated on the blow, the bi-level planning of stations selection& route not only guarantees the quickness of evacuating vehicles but also ensures the globality of evacuated personnel as a result that it provides the important model support for the efficient evacuation plan. In the upper level of decision, it focuses on considering establishing the optimal evacuation route for the evacuating public vehicles so that the victims are able to arrive in the safe area as soon as possible. Given the position of several evacuation assembly stations and the personnel arrangement of the evacuation community, the upper model is trying to solve the vehicle routing problem (shorted from VRP), whose objective function is the shortest time spent on evacuation (including the waiting time spent). In the lower level, it focuses on considering how to choose the optimal position of assembly stations and the personnel arrangement in order to evacuate as many victims as possible. Given the demand of the evacuation community& the alternative assembly stations, the objective function is the total coverage percent of demand met by the utmost efforts, and the question is to solve the Maximum Covering Location Problems (shorted from MCLP). Considering complexity of the bi-level programming model, there are many restrictions and it is non deterministic polynomial (shorted from NP-hard). In the situation we find the solution for model by genetic algorithm. Through the chromosome coding strategy, the best planning strategy are figured out quickly after selection, crossover and mutation process..更多还原