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J. Rail Transp. Plan. Manag.最新文献

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Pedestrian small group behaviour and evacuation dynamics on metro station platform 地铁站台行人群体行为与疏散动态
Pub Date : 2023-06-01 DOI: 10.2139/ssrn.3985805
Qi Zhang, Jing Qu, Yanzhe Han
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引用次数: 2
International benchmarking of railroad safety data systems and performance - a cross-continental case study 铁路安全数据系统和性能的国际基准-跨大陆案例研究
Pub Date : 2023-06-01 DOI: 10.2139/ssrn.4273487
Chen-yu Lin, M. Blumenfeld, Theodore Gerstein, C. Barkan, A. Jack, Usman T. Abdurrahman
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引用次数: 2
Railway freight wagon fleet size optimization: A real-world application 铁路货运车队规模优化:一个实际应用
Pub Date : 2023-06-01 DOI: 10.2139/ssrn.4259864
M. Milenković, N. Bojovic, Dmitry Abramin
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引用次数: 4
Inequity averse optimization of railway traffic management considering passenger route choice and Gini Coefficient 考虑客运路线选择和基尼系数的铁路交通管理不公平优化
Pub Date : 2023-06-01 DOI: 10.2139/ssrn.4033217
X. Luan, Xiao Sun, F. Corman, L. Meng
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引用次数: 0
Optimal resource rescheduling in classification yards considering flexible skill patterns 考虑灵活技能模式的船级社资源调度优化
Pub Date : 2023-06-01 DOI: 10.2139/ssrn.4193391
Henning Preis, Tobias Pollehn, Moritz Ruf
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引用次数: 0
Multiobjective optimization of railway cold-chain transportation route based on dynamic train information 基于列车动态信息的铁路冷链运输路线多目标优化
Pub Date : 2023-06-01 DOI: 10.2139/ssrn.4194312
Shouchen Liu, Cheng Zhang
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引用次数: 1
An evaluation of the fairness of railway timetable rescheduling in the presence of competition between train operators 列车营运商之间存在竞争时,对铁路时间表改期公平性的评估
Pub Date : 2023-06-01 DOI: 10.2139/ssrn.4033218
E. Reynolds, M. Ehrgott, Judith Y. T. Wang
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引用次数: 0
The cost differential for an optimal train journey on level track 水平轨道上最优列车行程的成本差异
Pub Date : 2023-06-01 DOI: 10.2139/ssrn.4253410
P. Howlett, Peter Pudney
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引用次数: 0
A survey on the acceptance of unattended trains 关于无人值守列车接受情况的调查
Pub Date : 2023-03-01 DOI: 10.2139/ssrn.4010336
Albrecht Morast, G. Voß, Pia S. C. Dautzenberg, P. Urbán, Nils Nießen
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引用次数: 2
A review of train delay prediction approaches 列车延误预测方法综述
Pub Date : 2022-06-01 DOI: 10.2139/ssrn.3964737
Thomas Spanninger, Alessio Trivella, Beda Büchel, F. Corman
Railway operations are vulnerable to delays. Accurate predictions of train arrival and departure delays improve the passenger service quality and are essential for real-time railway traffic management to minimise their further spreading. This review provides a synoptic overview and discussion covering the breadth of diverse approaches to predict train delays. We first categorise research contributions based on their underlying modelling paradigm (data-driven and event- driven) and their mathematical model. We then distinguish between very short to long-term predictions and classify different input data sources that have been considered in the literature. We further discuss advantages and disadvantages of producing deterministic versus stochastic predictions, the applicability of different approaches during disruptions and their interpretability. By comparing the results of the included contributions, we can indicate that the prediction error generally increases when broadening the prediction horizon. We find that data-driven approaches might have the edge on event-driven approaches in terms of prediction accuracy, whereas event-driven approaches that explicitly model the dynamics and dependencies of railway traffic have their strength in providing interpretable predictions, and are more robust concerning disruption scenarios. The growing availability of railway operations data is expected to increase the appeal of big-data and machine learning methods.
铁路运营容易出现延误。列车到达和离开延误的准确预测提高了旅客服务质量,对于实时铁路交通管理至关重要,以尽量减少其进一步蔓延。这篇综述提供了一个概括性的概述和讨论,涵盖了预测火车延误的各种方法的广度。我们首先根据其潜在的建模范式(数据驱动和事件驱动)及其数学模型对研究贡献进行分类。然后,我们区分短期和长期预测,并对文献中考虑的不同输入数据源进行分类。我们进一步讨论了产生确定性与随机预测的优缺点,不同方法在中断期间的适用性及其可解释性。通过比较纳入贡献的结果可以看出,随着预测范围的扩大,预测误差普遍增大。我们发现,数据驱动的方法在预测精度方面可能比事件驱动的方法更具优势,而明确模拟铁路交通动态和依赖关系的事件驱动方法在提供可解释的预测方面具有优势,并且在中断场景方面更加稳健。铁路运营数据的日益可用性预计将增加大数据和机器学习方法的吸引力。
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引用次数: 18
期刊
J. Rail Transp. Plan. Manag.
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