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A Robust Timetabling Model for a Metro Line with Passenger Activity Information

机译：具有乘客活动信息的地铁线路的稳健时间表模型

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摘要

Timetable design is crucial to the reliability of a metro service. In terms of the delays caused by passengers’ boarding and alighting behaviors during rush hours, the planned timetable for a metro line with high-frequency service tends to be difficult to implement. General oversaturation events, rather than accidents or track damage, still have a significant impact on metro systems, so that trains are canceled and delayed. When the activity reality diverges from the real-time or historical information, it is imperative that dispatchers present a good solution during the planning stage in order to minimize the nuisance for passengers and reduce the crowding risk. This paper presents a robust timetabling model (RTM) for a metro line with passenger activity information, which takes into account congestion and buffer time adjustments. The main objective pursued by dispatchers in the model is the enhancement of punctuality while minimizing train delays by adjusting the buffer time. By explicitly taking the passenger activity information into account, a mixed integer nonlinear programming (MINLP) model was developed, and a genetic algorithm (GA) is proposed to solve the model. Finally, numerical experiments based on the Batong line of the Beijing Metro were carried out, the results of which verify the effectiveness and efficiency of our method.
机译：时间表设计对于地铁服务的可靠性至关重要。就高峰时段乘客上下车行为造成的延误而言，地铁高频线路的计划时间表往往难以实施。一般的过饱和事件，而不是事故或轨道损坏，仍然会对地铁系统产生重大影响，因此火车被取消和延迟。当活动现实与实时或历史信息有所不同时，调度员必须在计划阶段提出一个好的解决方案，以最大程度地减少对乘客的滋扰并降低拥挤风险。本文针对具有乘客活动信息的地铁线，提出了一种健壮的时间表模型（RTM），该模型考虑了拥堵和缓冲时间调整。该模型中调度员追求的主要目标是通过调整缓冲时间来提高守时性，同时最大程度地减少火车延误。通过明确考虑乘客活动信息，建立了混合整数非线性规划（MINLP）模型，并提出了遗传算法（GA）对该模型进行求解。最后，基于北京地铁八通线进行了数值试验，结果验证了该方法的有效性和有效性。

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