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基于不确定需求的公共交通网络鲁棒性优化方法
引用本文:周康,彭虓,宋瑞. 基于不确定需求的公共交通网络鲁棒性优化方法[J]. 计算机应用研究, 2020, 37(7): 2006-2010
作者姓名:周康  彭虓  宋瑞
作者单位:交通运输部科学研究院,北京 100029;北京交通大学 城市交通复杂系统理论与技术教育部重点实验室,北京 100044;北京交通大学 城市交通复杂系统理论与技术教育部重点实验室,北京 100044;交通运输部科学研究院,北京 100029
基金项目:国家重点研发计划;国家科技支撑计划;国家自然科学基金
摘    要:为了提高城市不同类型公共交通所组成的线网的鲁棒性,从公共交通线路建设成本、乘客出行的总时间以及乘客总换乘次数等方面确定公共交通网络的服务性能模型,在此基础上通过计算方案目标值与期望值的差值来确定公交网络的鲁棒性;由于存在随机不确定需求,在传统免疫克隆算法基础上对变异操作进行改进用于对优化模型求解。结合算例分析发现,线路建设成本、乘客总出行时间以及乘客总换乘次数的参数值对于优化结果具有显著影响;另外鲁棒性参数取值也会对计算结果产生一定影响,通过算例验证了优化方法的可行性。

关 键 词:城市交通  鲁棒性优化  免疫克隆算法  公共交通网络  不确定需求
收稿时间:2018-12-27
修稿时间:2020-06-04

Robust optimization method for public transport network based on uncertain demand
Zhou Kang,Peng Xiao and Song Rui. Robust optimization method for public transport network based on uncertain demand[J]. Application Research of Computers, 2020, 37(7): 2006-2010
Authors:Zhou Kang  Peng Xiao  Song Rui
Affiliation:MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University,,
Abstract:In order to improve the robustness of the network composed of different modes of urban public transport, from the aspects of construction cost of public transport line, total travel time of passengers and total transfer times of passengers to construct the service performance model of public transport network. On this basis, it determined the robustness of the public transport network by calculating the D-value between the target value and the expected value. Due to the existence of uncertain demand, this paper improved the mutation operation based on the traditional immune clonal algorithm to solve the optimization model. It is found that the parameters of route construction cost, total passenger travel time and total passenger transfer times have a significant impact on the optimization results. In addition, the robustness parameters also have a certain impact on the calculation results. From the case study, it verifies the feasibility of the optimization method and found some problems that need to be improved.
Keywords:urban traffic   robust optimization   immune clonal algorithm   public transport networks   uncertain demand
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