A microscopic evaluation of railway timetable robustness and critical points

Solinen, Emma; Nicholson, Gemma; Peterson, Anders

doi:10.1016/j.jrtpm.2017.08.005

Cited by 19 publications

(11 citation statements)

References 18 publications

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“…The studied railway stretch is part of the Scandinavian-Mediterranean freight rail corridor, and connects continental Europe to Hallsberg, the largest marshalling yard in Scandinavia; substretches are, for example, considered in Khoshniyat and Peterson (2017) and Solinen et al (2017), and references therein.…”

Section: Experimental Study: Malmö-hallsbergmentioning

confidence: 99%

Railway timetabling: a maximum bottleneck path algorithm for finding an additional train path

et al. 2020

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We present an algorithm to insert a train path in an existing railway timetable close to operation, when we want to affect the existing (passenger) traffic as little as possible. Thus, we consider all other trains as fixed, and aim for a resulting train path that maximizes the bottleneck robustness, that is, a train path that maximizes the temporal distance to neighboring trains in the timetable. Our algorithm is based on a graph formulation of the problem and uses a variant of Dijkstra’s algorithm. We present an extensive experimental evaluation of our algorithm for the Swedish railway stretch from Malmö to Hallsberg. Moreover, we analyze the size of our constructed graph.

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Section: Experimental Study: Malmö-hallsbergmentioning

confidence: 99%

Railway timetabling: a maximum bottleneck path algorithm for finding an additional train path

et al. 2020

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“…Furthermore, in order to increase the robustness of timetable, Andersson et al [2] presented the robustness in critical points (RCP) to formulate a MILP model with the objective to add the time margins in the critical points while minimizing the deviation from the planned timetable. Solinen et al [12] presented the implementation of RCP and determined the impact of increased RCP value on the timetable performance. Abid et al [13] extended the concept of RCP to single-track train timetables.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Inequality (11) ensures that no train will start before the prespecified time at the origin. An activity cannot take more time than the duration of that activity that is applied by (12). Equation (13) puts the constraint on the minimum dwell times at the stations and (14) is to ensure the safe operations along the corridor by placing headways.…”

Section: ( )mentioning

confidence: 99%

Increasing Robustness by Reallocating the Margins in the Timetable

Meng

Abid

Jiang

et al. 2019

Journal of Advanced Transportation

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It is a common practice to improve the punctuality of a railway service by the addition of time margins during the planning process of a timetable. Due to the capacity constraints of the railway network, a limited amount of time margins can be inserted. The paper presents a model and heuristic technique to find the better position for the limited amount of time margins (headway buffers and running time supplements) in a train timetable. The aim of reallocating the time margins is to adjust an existing timetable to minimize the sum of train delays at the event of the operational disturbances. The model consists of two basic parts. Firstly, the paper treats the train timetable as a Directed Arc Graph (DAG) with the aggregation concept and proposes a heuristic technique known as Critical Time Margins Allocation (CTMA), which is based on the critical path method (CPM), to reallocate the time margins. Secondly, the paper evaluates the original and modified timetable under different disturbed situations. The case study is developed on a hypothetical small railway network and a practical timetable of single-line train timetable for the track segment of Rawalpindi to Lalamusa, Pakistan. The results show that the timetable modified with the CTMA reduces the total delay time by an average of 3.25% for the small railway network and 5.18% for the large dataset. It suggests that adding the time supplements to the proper positions in a timetable can reduce the delay propagation and increase the robustness of the timetable.

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“…In the adjusted case, total delays at the end station are 28% lower. Solinen et al [74] also use an optimisation model to increase the RCP throughout a timetable and evaluate the results with simulation. They found that robustness increased locally, but that the relationship between ex post measures and RCP must be studied further.…”

Section: Robust Timetablesmentioning

confidence: 99%

The Planners’ Perspective on Train Timetable Errors in Sweden

Palmqvist

Olsson

Hiselius

2018

Journal of Advanced Transportation

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Timetables are important for train punctuality. However, relatively little attention has been paid to the people who plan the timetables: the research has instead been more centred on how to improve timetables through simulation, optimisation, and data analysis techniques. In this study, we present an overview of the state of practice and the state of the art in timetable planning by studying the research literature and railway management documents from several European countries. We have also conducted interviews with timetable planners in Southern Sweden, focusing on how timetable planning relates to punctuality problems. An important backdrop for this is a large project currently underway at the Swedish Transport Administration, modernizing the timetable planning tools and processes. This study is intended to help establish a baseline for the future evaluation of this modernization by documenting the current process and issues, as well as some of the research that has influenced the development and specifications of the new tools and processes. Based on the interviews, we found that errors in timetables commonly lead to infeasible timetables, which necessitate intervention by traffic control, and to delays occurring, increasing, and spreading. We found that the timetable planners struggle to create a timetable and that they have neither the time nor the tools required to ensure that the timetable maintains a high quality and level of robustness. The errors we identified are (a) crossing train paths at stations, (b) wrong track allocation of trains at stations, especially for long trains, (c) insufficient dwell and meet times at stations, and (d) insufficient headways leading to delays spreading. We have identified eleven reasons for these errors and found three themes among these reasons: (1) “missing tools and support,” (2) “role conflict,” and (3) “single-loop learning.” As the new tools and processes are rolled out, the situation is expected to improve with regard to the first of these themes. The second theme of role conflict occurs when planners must strive to meet the demands of the train operating companies, while they must also be unbiased and create a timetable that has a high overall quality. While this role conflict will remain in the future, the new tools can perhaps help address the third theme by elevating the planners from first- to double-loop learning and thereby allowing them to focus on quality control and on finding better rules and heuristics. Over time, this will lead to improved timetable robustness and train punctuality.

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A microscopic evaluation of railway timetable robustness and critical points

Cited by 19 publications

References 18 publications

Railway timetabling: a maximum bottleneck path algorithm for finding an additional train path

Railway timetabling: a maximum bottleneck path algorithm for finding an additional train path

Increasing Robustness by Reallocating the Margins in the Timetable

The Planners’ Perspective on Train Timetable Errors in Sweden

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