Integrated timetable rescheduling and passenger reassignment during railway disruptions

Zhu, YC Youcai; Goverde, Rob M.P.

doi:10.1016/j.trb.2020.09.001

Cited by 35 publications

(12 citation statements)

References 25 publications

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“…However, the same type of occurrence is found, but with different resolution times. Zhu and Goverde ( 34 ) propose a mixed integer linear program available in the second phase of the bathtub model to calculate the impact of getting passengers to their destinations in situations of disturbed traffic. In this proposal every decision is relevant, taking into account passenger demand.…”

Section: Resultsmentioning

confidence: 99%

Literature Review on Problem Models and Solution Approaches for Managing Real-Time Passenger Train Operations: The Perspective of Train Operating Companies

Marques

Moro

Ramos

2022

Transportation Research Record: Journal of the Transportation R

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This study presents a comprehensive review of different problem models for managing railway operations by problem-type classification. Railway terminology was used to identify the studies that encompass the existing body of knowledge. The 28 articles analyzed showed that existing studies are focused on the individual schedule components, such as rolling stock, schedules, crews, and passengers. Few studies have adopted a broader scope by covering several of those components. Two of the most popular approaches include the integer linear program and the mixed integer linear program variant. The difference between them is that integer programming uses discrete decision-making variable data, while mixed integer programming also admits continuous variable data. In contrast, few studies involve combining computational algorithms with human knowledge-based approaches. This analysis reveals that the most significant variables for managing disruptive events are related to verifying suppressed circulation and the discrete events of real-time traffic, such as departures and arrivals at stations.

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Section: Resultsmentioning

confidence: 99%

Literature Review on Problem Models and Solution Approaches for Managing Real-Time Passenger Train Operations: The Perspective of Train Operating Companies

Marques

Moro

Ramos

2022

Transportation Research Record: Journal of the Transportation R

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“…As future work, computation times could be improved by applying advanced simulation-based optimization approaches or decomposition techniques such as rolling horizon. In addition, passenger demand could be approximated by clustering in passenger groups instead of modelling single passengers as in [19]. Moreover, linearizing PFM model can also be explored.…”

Section: Discussionmentioning

confidence: 99%

“…Zhu and Goverde [18] used this model to determine passengers' paths and the passenger-dependent weights in the objective function, with the aim of minimizing the cancelled trains, number of skipping stops and passenger delays. Zhu and Goverde [19] extended this approach to an integrated timetable rescheduling and passenger reassignment model, and they proposed an Adapted Fix-and-Optimize algorithm to solve the MILP. During disruption, the large number of passengers in a metro line will cause severe congestion and increase the passengers' waiting time.…”

Section: Literature Reviewmentioning

confidence: 99%

A Matheuristic for the Integrated Disruption Management of Traffic, Passengers and Stations in Urban Railway Lines

Bešinović

Wang

Zhu

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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In big cities, the metro lines usually face great pressure caused by huge passengers demand, especially during peak hours. When disruptions occur, passengers accumulate quickly at stations. It is of great importance for dispatchers to take passenger flow control into consideration for the traffic management to ensure passengers' safety and to maintain their satisfaction. This paper proposes an integrated disruption management model, which incorporates train rescheduling and passenger flow control. In this model, the train services can be short-turned, cancelled and rerouted, while the number of passengers entering a station is managed by controlling the station gates with consideration of the capacities of platforms and trains. Moreover, the number of passengers arriving at a station is calculated according to the origin-destination matrices. The objectives are to recover the train operation to the original timetable as soon as possible and to minimize the waiting time of passengers outside the stations. With the interaction between train services, passengers and station gates, an iterative metaheuristic approach is proposed to solve the integrated disruption management problem. Based on the data of a Beijing metro line, numerical experiments are conducted to test the proposed algorithm. The results demonstrate the importance of integrated disruption management and the effectiveness of our solution method.

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“…Zhu and Goverde (2020a) extend this model into a rolling horizon two-stage stochastic programming problem to deal with uncertainties of disruption durations. Zhu and Goverde (2020b) propose an integrated timetable rescheduling and passenger reassignment model during railway disruptions that extends the models in Zhu and Goverde (2019a, b) towards passenger-oriented timetable rescheduling.…”

Section: Timetable Reschedulingmentioning

confidence: 99%

A next step in disruption management: combining operations research and complexity science

et al. 2021

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Railway systems occasionally get into a state of being out-of-control, meaning that barely any train is running, even though the required resources (infrastructure, rolling stock and crew) are available. Because of the large number of affected resources and the absence of detailed, timely and accurate information, currently existing disruption management techniques cannot be applied in out-of-control situations. Most of the contemporary approaches assume that there is only one single disruption with a known duration, that all information about the resources is available, and that all stakeholders in the operations act as expected. Another limitation is the lack of knowledge about why and how disruptions accumulate and whether this process can be predicted. To tackle these problems, we develop a multidisciplinary framework combining techniques from complexity science and operations research, aiming at reducing the impact of these situations and—if possible—avoiding them. The key elements of this framework are (i) the generation of early warning signals for out-of-control situations, (ii) isolating a specific region such that delay stops propagating, and (iii) the application of decentralized decision making, more suited for information-sparse out-of-control situations.

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Integrated timetable rescheduling and passenger reassignment during railway disruptions

Cited by 35 publications

References 25 publications

Literature Review on Problem Models and Solution Approaches for Managing Real-Time Passenger Train Operations: The Perspective of Train Operating Companies

Literature Review on Problem Models and Solution Approaches for Managing Real-Time Passenger Train Operations: The Perspective of Train Operating Companies

A Matheuristic for the Integrated Disruption Management of Traffic, Passengers and Stations in Urban Railway Lines

A next step in disruption management: combining operations research and complexity science

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