A real-time conflict solution algorithm for the train rescheduling problem

Bettinelli, Andrea; Santini, Alberto; Vigo, Daniele

doi:10.1016/j.trb.2017.10.005

Cited by 55 publications

(38 citation statements)

References 38 publications

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“…More information regarding MPI can be accessed via the online book of Barney [34]. In railway research, the MPI has found many applications [7,[11][12][13][21][22][23]31,[35][36][37][38][39][40][41][42][43]; it is one of the most widely used parallel computing enabling techniques. In the authors' opinion, MPI is very suitable for parallel co-simulations and parallel optimisations, as presented in References [31] and [42] respectively, since they have very independent parallelised computing tasks.…”

Section: Mpimentioning

confidence: 99%

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Parallel computing in railway research

Spiryagin

Cole

et al. 2018

International Journal of Rail Transportation

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Available computing power for researchers has been increasing exponentially over the last decade. Parallel computing is possibly the best way to harness computing power provided by multiple computing units. This paper reviews parallel computing applications in railway research as well as the enabling techniques used for the purpose. Nine enabling techniques were reviewed and Message Passing Interface, Domain Decomposition and Hadoop & Apache are the top three most widely used enabling techniques. Seven major application topics were reviewed and iterative optimisations, continuous dynamics and data & signal analysis are the most widely reported applications. The reasons why these applications are suitable for parallel computing were discussed as well as the suitability of various enabling techniques for different applications. Computing time speed-ups that were reported from these applications were summarised. The challenges for applying parallel computing for railway research are discussed. ARTICLE HISTORY

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Section: Mpimentioning

confidence: 99%

“…In railway research, parallel computing applications have been widely reported for train scheduling optimisations with different optimisation algorithms. Huang et al [76] have used GA, Boman and Gerdovci [8] and Bettinelli et al [43] used the Tabu optimisation method. Boman and Gerdovci [8] also used SA.…”

Section: Iterative Optimisationmentioning

confidence: 99%

Parallel computing in railway research

Spiryagin

Cole

et al. 2018

International Journal of Rail Transportation

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“…For safety reasons, the trains have to obey a minimum clear time and headway time distance [23]. The clear time (c m ) is the minimum time that a train j must wait before entering a section m, which Algorithms 2018, 11, 55 9 of 18 the train j just left.…”

Section: Less Total Processing Timementioning

confidence: 99%

“…The use of task parallelization to reduce computation time was explored by Bettinelli et al [23], who proposed a parallel algorithm for the train scheduling problem. The problem is modelled by a graph.…”

Section: Introductionmentioning

confidence: 99%

A Heuristic Approach to Solving the Train Traffic Re-Scheduling Problem in Real Time

Gholami

Krasemann

2018

Algorithms

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Effectiveness in managing disturbances and disruptions in railway traffic networks, when they inevitably do occur, is a significant challenge, both from a practical and theoretical perspective. In this paper, we propose a heuristic approach for solving the real-time train traffic re-scheduling problem. This problem is here interpreted as a blocking job-shop scheduling problem, and a hybrid of the mixed graph and alternative graph is used for modelling the infrastructure and traffic dynamics on a mesoscopic level. A heuristic algorithm is developed and applied to resolve the conflicts by re-timing, re-ordering, and locally re-routing the trains. A part of the Southern Swedish railway network from Karlskrona centre to Malmö city is considered for an experimental performance assessment of the approach. The network consists of 290 block sections, and for a one-hour time horizon with around 80 active trains, the algorithm generates a solution in less than ten seconds. A benchmark with the corresponding mixed-integer program formulation, solved by commercial state-of-the-art solver Gurobi, is also conducted to assess the optimality of the generated solutions.

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“…Kersbergen et al [21] presented a dynamic model predictive control for railway traffic management which can improve calculation speeds and achieve good performances. Bettinelli et al [22] proposed a real-time processing algorithm to avoid potential conflicts for railway traffic management. Eaton et al [23] explored the ability of ant colony optimization to engender nondominated solutions for the dynamic multiobjective train rescheduling problem.…”

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 real-time conflict solution algorithm for the train rescheduling problem

Cited by 55 publications

References 38 publications

Parallel computing in railway research

Parallel computing in railway research

A Heuristic Approach to Solving the Train Traffic Re-Scheduling Problem in Real Time

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

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