考虑换乘异质性的城市轨道交通时刻表协同优化模型

在城市轨道交通网络化运营条件下,极易导致换乘站的换乘需求差异过大。为提高列车时刻表与换乘需求的匹配度,本文基于网络中换乘站的空间拓扑结构和换乘需求在时间和方向上的特点,通过构建量化换乘差异的协同度指标,建立以列车同步次数最大化为目标的列车时刻表优化模型,优化轨道交通网络线路间成功衔接次数,提升乘客换乘出行效率。针对提出的混合整数非线性规划模型,本文设计了一种基于天牛须搜索的粒子群优化算法进行求解,并将模型及算法应用于北京市轨道交通网络进行算例分析。结果表明,所构建的模型能依据换乘需求在空间、时间及方向上的差异,利用协同度分级优化轨道交通路网中列车协同状态;优化后全网列车同步到达次数增加33.86%,乘客平均换乘等待时间减少22.75%;相较于PSO和BAS算法,本文所提的算法具有更好的全局搜索能力和求解效率。本文可有效提高轨道交通换乘效率,为提升城市轨道交通服务质量提供理论参考。

Train Timetable Collaborative Optimization Model ConsideringTransfer Heterogeneity for Urban Rail Transit System

Under the background of urban rail transit network operation, there exists significant heterogeneity oftransfer demand at the transfer stations. This paper develops the indicator of coordination degree to quantify transferheterogeneity, according to the transit network topological characteristic of the transfer stations, and various transferdemands at different times and directions. Then, a train timetable optimization model is built to optimize the number oftrain synchronization and improve passenger transfer efficiency. Besides, a particle swarm optimization algorithmbased on the beetle antennae search is designed to solve the proposed mixed-integer nonlinear programming model. Wethen test our approaches by a case study of the Beijing rail transit network. The results show that (1) the presentedmodel can optimize the train coordination for the urban rail transit network based on the indicator of coordinationdegree, (2) the number of train synchronization can be improved by 33.86% and the average passenger waiting timecan be reduced by 22.75%, and (3) the designed algorithm is of better performance than the primary PSO and BASalgorithms in the aspects of the global search ability and solving efficiency. Summarily, our approaches cansignificantly improve the efficiency of urban rail transit transfer and provide theoretical references for improving thequality of urban rail transit service.