A case study on the coordination of last trains for the Beijing subway network

Kang, Liujiang; Wu, Jianjun; Sun, Huijun; Zhu, Xiaomin; Gao, Ziyou

doi:10.1016/j.trb.2014.09.003

Cited by 123 publications

(71 citation statements)

References 25 publications

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“…Coordinated departure times for all last trains are determined by the model. Kang et al [2] established a programing model with adjustable running times and dwell times to obtain coordinated arrival and departure times of last trains at transfer stations. A genetic algorithm was designed to solve the model.…”

Section: Last-trainmentioning

confidence: 99%

“…The model was solved by a genetic simulated annealing algorithm. Kang and Zhu [4] studied the same problem in Kang et al [2] and designed a new heuristic algorithm outperforming both genetic algorithms and simulated annealing algorithms.…”

Section: Last-trainmentioning

confidence: 99%

“…Recently, there are several contributions to the last-train timetabling problem of a subway system, which focused only on the last train of each line in the network and expected to generate a more efficiently scheduled timetable for all last trains [1][2][3][4]. However, it is very common that the scheduled timetable cannot be implemented because of unavoidable delays that occur at an operational level [5].…”

Section: Introductionmentioning

confidence: 99%

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A Practical Method for Timetable Rescheduling in Subway Networks during the End-of-Service Period

Zhao

Ning

2018

Journal of Advanced Transportation

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This study proposes a biobjective optimization method for timetable rescheduling during the end-of-service period of a subway network, taking all stakeholders' interests into consideration. We seek to minimize the total transfer waiting time for all transfer passengers, meanwhile minimizing the deviation to the scheduled timetable. The -constraint method and linearization techniques are utilized to obtain the approximate Pareto optimal solutions within limited seconds, allowing for figuring out the trade-off between the two objectives. The method is validated by numerical experiments for different delay scenarios based on a real-world case: the Beijing subway network.

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Section: Last-trainmentioning

confidence: 99%

Section: Last-trainmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Practical Method for Timetable Rescheduling in Subway Networks during the End-of-Service Period

Zhao

Ning

2018

Journal of Advanced Transportation

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“…Kang et al [10] proposed a rescheduling model for last trains with the consideration of train delays caused by incidents that occurred in train operations. Kang et al [11] established a lasttrain network transfer model to maximize passenger transfer connection headway, which reflected last-train connections and transfer waiting time. Li et al [12] studied the last-train coordination problem for metro networks based on actual passenger demand obtained from AFC system data, aiming to maximize the total number of passengers who can reach their destinations by metro prior to the end of operation.…”

Section: Literature Reviewmentioning

confidence: 99%

Last-Train Timetabling under Transfer Demand Uncertainty: Mean-Variance Model and Heuristic Solution

Yang

Gao

et al. 2017

Journal of Advanced Transportation

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Traditional models of timetable generation for last trains do not account for the fact that decision-maker (DM) often incorporates transfer demand variability within his/her decision-making process. This study aims to develop such a model with particular consideration of the decision-makers' risk preferences in subway systems under uncertainty. First, we formulate an optimization model for last-train timetabling based on mean-variance (MV) theory that explicitly considers two significant factors including the number of successful transfer passengers and the running time of last trains. Then, we add the mean-variance risk measure into the model to generate timetables by adjusting the last trains' departure times and running times for each line. Furthermore, we normalize two heterogeneous terms of the risk measure to provide assistance in getting reasonable results. Due to the complexity of MV model, we design a tabu search (TS) algorithm with specifically designed operators to solve the proposed timetabling problem. Through computational experiments involving the Beijing subway system, we demonstrate the computational efficiency of the proposed MV model and the heuristic approach.

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“…Taniguchi and Shimamoto (2004) presented a dynamic 32 vehicle scheduling model that incorporates real-time information using variable travel time. 33 Dynamic traffic simulation was utilized to update travel time.…”

mentioning

confidence: 99%

Timetable coordination of first trains in urban railway network: A case study of Beijing

Guo

Sun

et al. 2016

Applied Mathematical Modelling

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A case study on the coordination of last trains for the Beijing subway network

Cited by 123 publications

References 25 publications

A Practical Method for Timetable Rescheduling in Subway Networks during the End-of-Service Period

A Practical Method for Timetable Rescheduling in Subway Networks during the End-of-Service Period

Last-Train Timetabling under Transfer Demand Uncertainty: Mean-Variance Model and Heuristic Solution

Timetable coordination of first trains in urban railway network: A case study of Beijing

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