A data-driven, variable-speed model for the train timetable rescheduling problem
A data-driven, variable-speed model for the train timetable rescheduling problem
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DOI：
10.1016/j.cor.2022.105719
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发表时间：
2022-02
期刊：
影响因子：
--
通讯作者：
E. Reynolds;Stephen J. Maher
中科院分区：
文献类型：
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作者：
E. Reynolds;Stephen J. Maher
重新安排列车时刻表--实时改变列车进路和时刻以应对晚点的做法--有助于减少反动延误的影响。有许多现有的优化模型可用于确定在任何给定交通场景中重新安排时间表的最佳方式。然而，这些模型中的许多都没有充分考虑到列车达到重新安排的时刻表所需的加速和减速。少数几个能够解释这一点的模型过于复杂，无法在足够短的时间内解决到最优。在这项研究中，我们提出了一种新的列车时刻表调整模型，该模型使用统计方法和历史数据来简约地考虑列车速度。使用一组基于英国德比站真实数据的新实例对该模型进行了测试。我们表明，与固定速度的时刻表重新调度模型相比，所提出的模型在运行时间方面几乎没有折衷，但精确度有所提高。
Train timetable rescheduling — the practice of changing the routes and timings of trains in real-time to respond to delays — can help to reduce the impact of reactionary delay. There are a number of existing optimisation models that can be used to determine the best way to reschedule the timetable in any given traffic scenario. However, many of these models do not adequately account for the acceleration and deceleration required for trains to achieve the rescheduled timetable. The few models that do account for this are overly complex and cannot be solved to optimality in sufficiently short times. In this study, we propose a new model for train timetable rescheduling that uses statistical methods and historical data to parsimoniously take train speed into account. The model is tested using a new set of instances based on real data from Derby station in the UK. We show that the improved accuracy of the proposed model comes with little to no trade-off in terms of run time compared to fixed-speed timetable rescheduling models.