A demand-based weighted train delay approach for rescheduling railway networks in real time

Espinosa-Aranda, José Luís; García-Ródenas, Ricardo

doi:10.1016/j.jrtpm.2013.10.001

Cited by 32 publications

(22 citation statements)

References 29 publications

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“…The overall problem of controlling trains is split into subproblems along the time dimension (i.e., controlling only a slice in time of the problem), but only for combinatorial purposes [30], [66]; in some limited case [9], this also addresses limited information available. At every stage, the solver knows completely the future within the stage [6], [23], [66], [71].…”

Section: A Complete Knowledge Of the Futurementioning

confidence: 99%

A Review of Online Dynamic Models and Algorithms for Railway Traffic Management

Corman

Meng

2015

IEEE Trans. Intell. Transport. Syst.

197

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Railway timetables are developed to make operations robust and resilient to small delays. However, disturbances perturb the daily plan, and dispatchers adjust the plan to keep operations feasible and to limit delay propagation. Rescheduling approaches aim at updating the offline timetable at best, in the presence of delays. We present a survey of the recent approaches on online railway traffic rescheduling problems, which exhibit dynamic and stochastic (or, at least, not completely deterministic) aspects. In fact, while online static rescheduling has reached a wide degree of dissemination, much is still to be done with regard to the probabilistic nature of the railway traffic rescheduling problems, and also how to best take uncertainty into account for future states. Open challenges for the future research are finally outlined.

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Section: A Complete Knowledge Of the Futurementioning

confidence: 99%

A Review of Online Dynamic Models and Algorithms for Railway Traffic Management

Corman

Meng

2015

IEEE Trans. Intell. Transport. Syst.

197

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“…References [5] and [7] also demonstrate that integrated models provide better solutions for problems within the railway industry. Reference [13] presents a novel weighted train delay based on demand approach for rescheduling railway systems. Reference [16] describes a real-time rolling stock rescheduling model for disruption management which takes dynamic passenger flows into account.…”

Section: State Of Artmentioning

confidence: 99%

Smooth and Controlled Recovery Planning of Disruptions in Rapid Transit Networks

Cadarso

Maróti²,

Marı́n

2015

IEEE Trans. Intell. Transport. Syst.

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Abstract-This paper studies the disruption management problem of rapid transit rail networks. We consider an integrated model for the recovery of the timetable and the rolling stock schedules. We propose a new approach to deal with large-scale disruptions: we limit the number of simultaneous schedule changes as much as possible, and we control the length of the recovery period, in addition to the traditional objective criteria such as service quality and operational costs. Our new criteria express two goals: the recovery schedules can easily be implemented in practice, and the operations quickly return to the originally planned schedules after the recovery period. We report our computational tests on realistic problem instances of the Spanish rail operator RENFE and demonstrate the potential of this approach by solving different variants of the proposed model.

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“…Chevrier et al [15] explored the energy-efficient timetabling problem using multiobjective evolution approach. Espinosa-Aranda and García-Ródenas [16] put up a heuristic algorithm using alternative arcs to minimize the traveling delay time. Li et al [17] proposed the greedy algorithm to solve the train rescheduling problem with random delay.…”

Section: Introductionmentioning

confidence: 99%

Models and Algorithms of Conflict Detection and Scheduling Optimization for High-Speed Train Operations Based on MPC

Wang

Zhou

Deng

2018

Journal of Control Science and Engineering

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The high-speed train with high transport speed and punctuality rate has become a leading tool of daily traveling. Compared with the general-speed railway transportation, the high-speed railway transportation requires a timely and automatic adjustment capability in the centralized traffic control (CTC) system. In order to improve the capability, this paper mainly explores the models and algorithms of conflict detection and scheduling optimization of high-speed train operations. They are implemented under the mechanism of model predictive control (MPC) over the rolling prediction horizon. A train movement prediction model with conflict resolution is established. The mechanism of conflict detection and scheduling optimization is implemented through a case study. The simulation results demonstrate the effectiveness of the proposed mechanism.

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A demand-based weighted train delay approach for rescheduling railway networks in real time

Cited by 32 publications

References 29 publications

A Review of Online Dynamic Models and Algorithms for Railway Traffic Management

A Review of Online Dynamic Models and Algorithms for Railway Traffic Management

Smooth and Controlled Recovery Planning of Disruptions in Rapid Transit Networks

Models and Algorithms of Conflict Detection and Scheduling Optimization for High-Speed Train Operations Based on MPC

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