IDEAS home Printed from https://ideas.repec.org/a/spr/pubtra/v9y2017i1d10.1007_s12469-017-0156-0.html
   My bibliography  Save this article

A comparison of different configurations of a Centrally Guided Train Operation System in Dutch Railway Operations

Author

Listed:
  • Ramon M. Lentink

    (Netherlands Railways)

  • Dick Middelkoop

    (ProRail)

  • Douwe Vries

    (ProRail)

Abstract

Although Dutch train operation is one of the safest in the European Union, safety remains one of the top priorities. On a yearly basis, an estimated 7 million red signal approaches occur on the Dutch railway network for the largest train operating company NS Reizigers. These red signals alert a driver to prepare to stop the train, possibly because the next section of the track is occupied by another train. Out of these 7 million red signal approaches, 3 million red signal approaches are estimated to be caused by small deviations from the planning. As a result of this continuous focus on safety aspects, ProRail, the Dutch rail infrastructure manager, and NSR started a project to empower train drivers with more information on the current situation and near future related to their trains. In a simulation study four train driving strategies were compared in two areas in the network. These strategies, ranked in order of increasing level of driver information quality, are: first is driving at highest allowed speed, second is following the timetable without advisory speed information, third is using advisory speed information without changing train orders, and fourth is using advisory speed information with the possibility of changing train orders. At each location the timetable has been exposed to three increasing levels of disturbance scenarios. Results show that the advisory speeds strategy (third) reaps a large part of the safety benefits that the fourth (limited Centrally Guided Train Operation) strategy is able to achieve.

Suggested Citation

  • Ramon M. Lentink & Dick Middelkoop & Douwe Vries, 2017. "A comparison of different configurations of a Centrally Guided Train Operation System in Dutch Railway Operations," Public Transport, Springer, vol. 9(1), pages 273-284, July.
  • Handle: RePEc:spr:pubtra:v:9:y:2017:i:1:d:10.1007_s12469-017-0156-0
    DOI: 10.1007/s12469-017-0156-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12469-017-0156-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s12469-017-0156-0?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Mazzarello, Maura & Ottaviani, Ennio, 2007. "A traffic management system for real-time traffic optimisation in railways," Transportation Research Part B: Methodological, Elsevier, vol. 41(2), pages 246-274, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Andrea D'Ariano & Francesco Corman & Dario Pacciarelli & Marco Pranzo, 2008. "Reordering and Local Rerouting Strategies to Manage Train Traffic in Real Time," Transportation Science, INFORMS, vol. 42(4), pages 405-419, November.
    2. Pellegrini, Paola & Marlière, Grégory & Rodriguez, Joaquin, 2014. "Optimal train routing and scheduling for managing traffic perturbations in complex junctions," Transportation Research Part B: Methodological, Elsevier, vol. 59(C), pages 58-80.
    3. Xiaoming Xu & Keping Li & Lixing Yang & Ziyou Gao, 2019. "An efficient train scheduling algorithm on a single-track railway system," Journal of Scheduling, Springer, vol. 22(1), pages 85-105, February.
    4. Sato, Keisuke & Fukumura, Naoto, 2012. "Real-time freight locomotive rescheduling and uncovered train detection during disruption," European Journal of Operational Research, Elsevier, vol. 221(3), pages 636-648.
    5. Luan, Xiaojie & Wang, Yihui & De Schutter, Bart & Meng, Lingyun & Lodewijks, Gabriel & Corman, Francesco, 2018. "Integration of real-time traffic management and train control for rail networks - Part 2: Extensions towards energy-efficient train operations," Transportation Research Part B: Methodological, Elsevier, vol. 115(C), pages 72-94.
    6. Carlo Mannino & Alessandro Mascis, 2009. "Optimal Real-Time Traffic Control in Metro Stations," Operations Research, INFORMS, vol. 57(4), pages 1026-1039, August.
    7. Meloni, Carlo & Pranzo, Marco & Samà, Marcella, 2021. "Risk of delay evaluation in real-time train scheduling with uncertain dwell times," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).
    8. Van Thielen, Sofie & Corman, Francesco & Vansteenwegen, Pieter, 2018. "Considering a dynamic impact zone for real-time railway traffic management," Transportation Research Part B: Methodological, Elsevier, vol. 111(C), pages 39-59.
    9. Wenliang Zhou & Xiaorong You & Wenzhuang Fan, 2020. "A Mixed Integer Linear Programming Method for Simultaneous Multi-Periodic Train Timetabling and Routing on a High-Speed Rail Network," Sustainability, MDPI, vol. 12(3), pages 1-34, February.
    10. Thomas Albrecht, 2009. "The Influence of Anticipating Train Driving on the Dispatching Process in Railway Conflict Situations," Networks and Spatial Economics, Springer, vol. 9(1), pages 85-101, March.
    11. Xu, Peijuan & Corman, Francesco & Peng, Qiyuan & Luan, Xiaojie, 2017. "A train rescheduling model integrating speed management during disruptions of high-speed traffic under a quasi-moving block system," Transportation Research Part B: Methodological, Elsevier, vol. 104(C), pages 638-666.
    12. Burdett, R.L. & Kozan, E., 2009. "Techniques for inserting additional trains into existing timetables," Transportation Research Part B: Methodological, Elsevier, vol. 43(8-9), pages 821-836, September.
    13. Zhang, San-Tong & Chen, Yi-Chuan, 2011. "Simulation for influence of train failure on railway traffic flow and research on train operation adjusting strategies using cellular automata," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(21), pages 3710-3718.
    14. Gabriel E. Sánchez-Martínez & Nigel H. M. Wilson & Haris N. Koutsopoulos, 2017. "Schedule-free high-frequency transit operations," Public Transport, Springer, vol. 9(1), pages 285-305, July.
    15. Dewilde, Thijs & Sels, Peter & Cattrysse, Dirk & Vansteenwegen, Pieter, 2014. "Improving the robustness in railway station areas," European Journal of Operational Research, Elsevier, vol. 235(1), pages 276-286.
    16. Wang, Pengling & Goverde, Rob M.P., 2017. "Multi-train trajectory optimization for energy efficiency and delay recovery on single-track railway lines," Transportation Research Part B: Methodological, Elsevier, vol. 105(C), pages 340-361.
    17. Pellegrini, Paola & Rodriguez, Joaquin, 2013. "Single European Sky and Single European Railway Area: A system level analysis of air and rail transportation," Transportation Research Part A: Policy and Practice, Elsevier, vol. 57(C), pages 64-86.
    18. Min, Yun-Hong & Park, Myoung-Ju & Hong, Sung-Pil & Hong, Soon-Heum, 2011. "An appraisal of a column-generation-based algorithm for centralized train-conflict resolution on a metropolitan railway network," Transportation Research Part B: Methodological, Elsevier, vol. 45(2), pages 409-429, February.
    19. Corman, Francesco & D'Ariano, Andrea & Pacciarelli, Dario & Pranzo, Marco, 2010. "A tabu search algorithm for rerouting trains during rail operations," Transportation Research Part B: Methodological, Elsevier, vol. 44(1), pages 175-192, January.
    20. Krzysztof Marcjan & Lucjan Gucma & Kotkowska Diana, 2021. "The Collision Risk Management Method for Ships Navigating on Coastal Waters Based on Ship Domain and Near-Miss Concept," European Research Studies Journal, European Research Studies Journal, vol. 0(4), pages 127-146.

    More about this item

    Keywords

    Driver advisory system; Real time traffic management; Train rescheduling; Safety in train operation;
    All these keywords.

    JEL classification:

    • L92 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Railroads and Other Surface Transportation
    • R41 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:pubtra:v:9:y:2017:i:1:d:10.1007_s12469-017-0156-0. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.