期刊文献+

基于粒子群算法的城市轨道交通接运公交规划 被引量:5

Planning of Feeder Bus to the Urban Rail Transit Based on Particle Swarm Optimization
下载PDF
导出
摘要 根据"逐条布设,优化成网"的优化思想,采用将线路规划区域离散化处理的方法,以接运效率最大为接运公交路线优化目标函数,建立模型,并给出算例.结果表明,接运公交线路能较好地实现接运效率最大化问题,从而使社会和乘客效益最大. According to the optimization idea of one by one-by-point routing,optimization into network ',with the methods of discretization for the routing design area,the paper builds the model with the feeder bus optimization function aiming at the maximization of feeder efficiency.And the paper introduces the PSO algorithm and its calculation steps for the solution to the model,as well as the example of algorithm.The results show that the feeder bus routine designed based on this method can solve the problem of the maximum of feeder well,so as to make the benefits of social and passengers maximized.Therefore,this method is effective,and it can get the solution to the problem of the planning of feeder bus.
出处 《武汉理工大学学报(交通科学与工程版)》 2010年第4期780-783,共4页 Journal of Wuhan University of Technology(Transportation Science & Engineering)
基金 国家自然科学基金项目资助(批准号:50778141)
关键词 轨道交通 接运公交 粒子群算法 rail transit feeder bus PSO algorithm
  • 相关文献

参考文献7

二级参考文献19

  • 1孙宝林,李腊元,陈华.基于遗传算法的实时QoS多播路由优化算法[J].计算机应用,2004,24(11):1-3. 被引量:4
  • 2王兴海,陶志祥.江苏省沿江地区轨道交通线网布局研究[J].武汉理工大学学报(交通科学与工程版),2005,29(2):262-265. 被引量:4
  • 3孙爱充,硕士学位论文,1995年
  • 4何宗华,城市轻轨交通工程设计指南,1993年
  • 5王炜,城市规划理论与方法,1992年
  • 6蓝武王,都市公共汽车研究专集,1984年
  • 7Kennedy J, Eberhart R. Particle swarm optimization [A]. Proc of Int'l Conf on Neural Networks [C]. Piscataway: IEEE Press, 1995. 1942-1948.
  • 8Eberhart R, Kennedy J. A new optimizer using particle swarm theory [A]. Proc of Int'l Symposium on Micro Machine and Human Science [C]. Piscataway: IEEE Service Center, 1995. 39-43.
  • 9Shi Y, Eberhart R C. Fuzzy adaptive particle swarm optimization [A].In: Furuhashi T,Mckay B,eds. Proc Congress on Evolutionary Computation [C]. Piscataway: IEEE Press, 2001.
  • 10Lovbjerg M, Rasmussen T K, Krink T. Hybrid particle swarm optimiser with breeding and subpopulations [A]. In: Spector L,eds. Proc of Genetic and Evolutionary Computation Conference [C]. San Fransisco: Morgan Kaufmann Publishers Inc, 2001. 469-476.

共引文献463

同被引文献27

  • 1于晓冬,董红斌.一种改进的进化策略研究[J].微电子学与计算机,2009,26(2):58-61. 被引量:2
  • 2李世雄.上海轨道交通线网的换乘[J].城市轨道交通研究,2004,7(3):66-69. 被引量:7
  • 3曹玫,林小涵.基于遗传算法的城市轨道交通接运公交线网规划[J].武汉理工大学学报(交通科学与工程版),2005,29(4):568-570. 被引量:31
  • 4李倢,中村良平.城市空间人口密度模型研究综述[J].国外城市规划,2006,21(1):40-47. 被引量:23
  • 5ALEJANDRO T, DAVID A H, SERGIO R. Compa- ring operator and users costs of light rail, heavy rail and bus rapid transit over a radial public transport network[J]. Research in Traportation Economics, 2010,29: 231-242.
  • 6WHELAN G, CROCKETT J. An investigation of the willingness to pay toreduce rail overcrowding[C]/// International Conference on Choice Modeling, Harro- gate, England, 2009 : 159-168.
  • 7GALLO M, MONTELLA B, D ' ACIERNO L. The transit network design problem with elastic demand and internalisation of external costs: An application to rail frequency optimisation [J]. Transportation Research Part C : Emerging Technologies, 2011,19 (6) : 1276.
  • 8GUIHAIRE V, HAO J K. Transit network design and scheduling: A global review [J]. Transportation Research Part A: Policy and Practice,2008,42(10) : 1251.
  • 9. KEPAPTSOGLOU K, KARLAFTIS M. Transit route network design problem: review [ J ]. Journal of transportation engineering, 2009,135 (8) : 491.
  • 10BIELLI M, CARAMIA M, CAROTENUTO P. Genetic algorithms in bus network optimization [J]. Transportation Research Part C: Emerging Technologies, 2002,10 (1) : 19.

引证文献5

二级引证文献18

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部