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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 被引量:1

Robust optimization method for public transport network based on uncertain demand
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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。 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.
作者 周康 宋瑞 彭虓 Zhou Kang;Song Rui;Peng Xiao(China Academy of Transportation Sciences,Beijing 100029,China;MOE Key Laboratory for Urban Transportation Complex Systems Theory&Technology,Beijing Jiaotong University,Beijing 100044,China)
出处 《计算机应用研究》 CSCD 北大核心 2020年第7期2006-2010,共5页 Application Research of Computers
基金 国家自然科学基金资助项目(41471459) 国家重点研发计划项目(2018YFB1600900) 国家科技支撑计划资助项目(2018YFB1201402)。
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 urban traffic robust optimization immune clonal algorithm public transport network uncertain demand
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  • 1唐秋生,冯海峰,李维.基于复杂网络理论的长沙市公交网络拓扑性质研究[J].交通信息与安全,2013,31(4):49-52. 被引量:9
  • 2Kennedy J, Eberhart R C. Particle swarm optimization [A]//Proceedings of the 1995 IEEE International Conference on Neural Networks [C]. New York, USA:IEEE, 1995:1942-1948
  • 3Shi Y,Eberhart R C. A modified particle swarm optimizer[A]//Proceedings of the 1998 IEEE International Conference on Evolutionary Computation[C]. Piscataway,USA:IEEE, 1998: 67-73
  • 4Silva A,Neves A,Costa E. An empirical comparison of particle swarm and predator prey optimization//Lecture Notes in Computer Science. vol. 2464. Berlin:Springer, 2002:103-110
  • 5Zhang W J, Xie X F, Yang Z L. Hybrid particle swarm optimizer with mass extinction//International Conference on Communication, Circuits and Systems. 2002 : 1170-1173
  • 6Krink T,Vesterstrφm J S,Riget J. Particle swarm optimization with spatial particle extension//Proceedings of the Congress on Evolutionary Computation. 2002:1474-1479
  • 7Lφvbjerg M , Krink T . Extending particle swarm optimisers with self-organized criticality // Proeeedings of the Congress on Evolutionary Computation. 2002:1588-1593
  • 8Riget J, Vesterstrφm J S. A diversity-guided particle swarm optimizer-the ARPSO. Technical Report 2002-02, EVALife. Department of Computer Science,University of Aarhus, 2002
  • 9Dasgupta D. Artificial Immune System and Their Applications [M]. Springer-Verlag, 1999
  • 10Burnet F M. The Clonal Selection Theory of Acquired Immunity[M]. Cambridge University Press,1959

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