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Optimizing Bus Frequencies under Uncertain Demand: Case Study of the Transit Network in a Developing City

机译:不确定需求下的公交车频率优化:以发展中城市的公交网络为例

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摘要

Various factors can make predicting bus passenger demand uncertain. In this study, a bilevel programming model for optimizing bus frequencies based on uncertain bus passenger demand is formulated. There are two terms constituting the upper-level objective. The first is transit network cost, consisting of the passengers' expected travel time and operating costs, and the second is transit network robustness performance, indicated by the variance in passenger travel time. The second term reflects the risk aversion of decision maker, and it can make the most uncertain demand be met by the bus operation with the optimal transit frequency. With transit link's proportional flow eigenvalues (mean and covariance) obtained from the lower-level model, the upper-level objective is formulated by the analytical method. In the lower-level model, the above two eigenvalues are calculated by analyzing the propagation of mean transit trips and their variation in the optimal strategy transit assignment process. The genetic algorithm (GA) used to solve the model is tested in an example network. Finally, the model is applied to determining optimal bus frequencies in the city of Liupanshui, China. The total cost of the transit system in Liupanshui can be reduced by about 6% via this method.
机译:各种因素会使预测客运乘客需求变得不确定。在这项研究中,建立了用于基于不确定的公共汽车乘客需求来优化公共汽车频率的双层规划模型。有两个术语构成高层目标。第一个是公交网络成本,包括乘客的预期旅行时间和运营成本,第二个是公交网络的鲁棒性表现,由乘客旅行时间的差异表示。第二项反映了决策者的风险规避,它可以使公交车以最佳的运输频率满足最不确定的需求。利用从下层模型获得的公交线路比例流量特征值(均值和协方差),通过分析方法来制定上层目标。在较低层模型中,通过分析平均运输行程的传播及其在最佳策略运输分配过程中的变化来计算上述两个特征值。在示例网络中测试了用于求解模型的遗传算法(GA)。最后,该模型被用于确定中国六盘水市的最佳公交车频率。通过这种方法,六盘水市公交系统的总成本可降低约6%。

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  • 来源
    《Mathematical Problems in Engineering》 |2013年第6期|375084.1-375084.10|共10页
  • 作者单位

    School of Transportation, Southeast University, Nanjing 210096, China,Department of Civil and Environmental Engineering, University of Wisconsin, Madison, WI 53705, USA;

    School of Transportation, Southeast University, Nanjing 210096, China;

    School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 610031, China;

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