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首页> 外文期刊>Applied Mathematical Modelling >Comprehensive optimization of urban rail transit timetable by minimizing total travel times under time-dependent passenger demand and congested conditions
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Comprehensive optimization of urban rail transit timetable by minimizing total travel times under time-dependent passenger demand and congested conditions

机译：通过在与时间相关的乘客需求和交通拥堵的情况下最大限度地减少总行驶时间，全面优化城市轨道交通时刻表

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摘要

The comprehensive optimization of the timetables of urban rail transit systems under more realistic conditions is essential for their practical operation. Currently, most time-dependent timetabling models do not adequately consider train capacity and variable operation parameters. To bridge this gap, this study mainly investigates the timetable design problem of the urban rail transit system so as to adapt to time-dependent passenger demand under congested conditions by considering the variable number of trains, train running time, and train dwell time. Two nonlinear non-convex programming models are formulated to design timetables with the objective of minimizing the total passenger travel time (TTT) under the constraints of train operations, and passenger boarding and alighting processes. The difference between the two models is that one is a train-capacity unconstrained model and the other is a train-capacity constrained model. The proposed models are examined through real-world cases solved by the adaptive large neighborhood search algorithm. The results show that the first model can minimize passenger TTT under dynamic passenger demand, whereas the second can comprehensively optimize passenger TTT and meantime keep the train load factor within a reasonable level. Accordingly, it is concluded that the proposed models are more realistic.
机译：在更现实的条件下全面优化城市轨道交通系统的时间表对于其实际运行至关重要。当前，大多数与时间有关的时间表模型都没有充分考虑列车容量和可变运行参数。为了弥合这一差距，本研究主要研究了城市轨道交通系统的时间表设计问题，以便通过考虑可变数量的列车，列车运行时间和列车停靠时间来适应拥挤情况下随时间变化的乘客需求。制定了两个非线性非凸规划模型来设计时间表，目的是在火车运营，登机和下车过程的约束下，将总旅客旅行时间（TTT）降至最低。两种模型之间的区别在于，一种是火车容量不受约束的模型，另一种是火车容量受约束的模型。通过使用自适应大邻域搜索算法解决的实际案例检查提出的模型。结果表明，第一个模型可以在动态乘客需求下最小化乘客TTT，而第二个模型可以全面优化乘客TTT，同时将火车载客率保持在合理水平。因此，可以得出结论，所提出的模型更为现实。

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