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A train dispatching model in case of segment blockages by integrating the prediction of delay propagation

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Abstract

In the high-speed railway system, trains’ original timetable is often disturbed by some emergencies including geological disasters and equipment failures, which brings great influence to passengers. This paper proposes a real-time high-speed train dispatching model in case of segment blockages, where a railway network is considered. The model includes the following two parts. First, if the trains are not cancelled or decelerated after a blockage occurs, the scope of the affected trains and stations is roughly estimated via the prediction of delay propagation model. Second, with the overall delay as the objective function, this paper constructs a mixed integer nonlinear programming (MINLP) model by considering the following three adjustment strategies: cancellation, delayed departure and deceleration, where the safe headway of the train operation is guaranteed by the moving blocking principle. Furthermore, to reduce the computation complexity, the solution of the model is only considered within the scope obtained in the first stage. The model is verified by using a small railway network with Nanjing as the hub station, which shows that the model is useful for reducing the effect of a disruption on original timetable, especially in comparison with the First Scheduled First Served (FSFS) rule used in practice.

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References

  1. Higgins A, Kozan E, Ferreira L (1996) Optimal scheduling of trains on a single line track. Transp Res Part B: Methodol 30(2):147–161

    Article  Google Scholar 

  2. Louwerse I, Huisman D (2014) Adjusting a railway timetable in case of partial or complete blockades. Eur J Oper Res 235(3):583–593

    Article  MathSciNet  Google Scholar 

  3. Zhan S, Kroon LG, Veelenturf LP, Wagenaar JC (2015) Real-time high-speed train rescheduling in case of a complete blockage. Transp Res Part B: Methodol 78:182–201

    Article  Google Scholar 

  4. D’Ariano A, Pranzo M, Hansen IA (2007) Conflict resolution and train speed coordination for solving real-time timetable perturbations. IEEE Trans Intell Transp Syst 8(2):208–222

    Article  Google Scholar 

  5. Dollevoet T, Corman F, D’Ariano A, Huisman D (2014) An iterative optimization framework for delay management and train scheduling. Flex Serv Manuf J 26(4):490–515

    Article  Google Scholar 

  6. Wang Y, Liao Z, Tao T, Ning B (2017) Train scheduling and circulation planning in urban rail transit lines. Control Eng Pract 61:112–123

    Article  Google Scholar 

  7. Zhan S, Kroon LG, Zhao J, Peng Q (2016) A rolling horizon approach to the high speed train rescheduling problem in case of a partial segment blockage. Transp Res Part E: Logist Transp Rev 95:32–61

    Article  Google Scholar 

  8. Xu P, Corman F, Peng Q, Luan X (2017) A train rescheduling model integrating speed management during disruptions of high-speed traffic under a quasi-moving block system. Transp Res Part B: Methodol 104:S0191261516305227

    Article  Google Scholar 

  9. Luan X, Wang Y, De Schutter B, Meng L, Lodewijks G, Corman F (2018) Integration of real-time traffic management and train control for rail networks-part 1: optimization problems and solution approaches. Transp Res Part B: Methodol 115:41–71

    Article  Google Scholar 

  10. Luan X, Wang Y, De Schutter B, Meng L, Lodewijks G, Corman F (2018) Integration of real-time traffic management and train control for rail networks-part 2: Extensions towards energy-efficient train operations. Transp Res Part B: Methodol 115:72–94

    Article  Google Scholar 

  11. Dong H, Zhu H, Li Y, Lv Y, Gao S, Zhang Q, Ning B (2018) Parallel intelligent systems for integrated high-speed railway operation control and dynamic scheduling. IEEE Trans Cybern 48(12):3381–3389

    Article  PubMed  Google Scholar 

  12. Corman F, D’Ariano A, Hansen IA, Pacciarelli D (2011) Optimal multi-class rescheduling of railway traffic. J Rail Transp Plann Manag 1(1):14–24

    Google Scholar 

  13. Corman F, D’Ariano A, Pacciarelli D, Pranzo M (2012) Bi-objective conflict detection and resolution in railway traffic management. Transp Res Part C: Emerg Technol 20(1):79–94

    Article  Google Scholar 

  14. Corman F, D’Ariano A, Pacciarelli D, Pranzo M (2012) Optimal inter-area coordination of train rescheduling decisions. Transp Res Part E: Logist Transp Rev 48(1):71–88

    Article  Google Scholar 

  15. Corman F, D’Ariano A, Pacciarelli D, Pranzo M (2014) Dispatching and coordination in multi-area railway traffic management. Comput Operat Res 44:146–160

    Article  Google Scholar 

  16. Thielen SV, Corman F, Vansteenwegen P (2018) Considering a dynamic impact zone for real-time railway traffic management. Transp Res Part B: Methodol 111:39–59

    Article  Google Scholar 

  17. Carey M, Kwieciński A (1994) Stochastic approximation to the effects of headways on knock-on delays of trains. Transp Res Part B: Methodol 28(4):251–267

    Article  Google Scholar 

  18. Huisman T, Boucherie RJ (2001) Running times on railway sections with heterogeneous train traffic. Transp Res Part B: Methodol 35(3):271–292

    Article  Google Scholar 

  19. Goverde RM (2007) Railway timetable stability analysis using max-plus system theory. Transp Res Part B: Methodol 41(2):179–201

    Article  Google Scholar 

  20. Landex A, Kaas AH, Nielsen OA (2008) Methods to estimate railway capacity and passenger delays. Technical University of Denmark (DTU)

  21. Jensen LW, Landex A, Nielsen OA, Kroon LG, Schmidt M (2017) Strategic assessment of capacity consumption in railway networks: framework and model. Transp Res Part C: Emerg Technol 74:126–149

    Article  Google Scholar 

  22. Harrod S, Cerreto F, Nielsen OA (2019) A closed form railway line delay propagation model. Transp Res Part C: Emerg Technol 102:189–209

    Article  Google Scholar 

  23. Shrithi Bhagya G, Emmanouil C, Stephan T, Felix A, Constantinos A (2021) An estimation framework to quantify railway disruption parameters. IET Intell Transp Syst 15(10):1256–1268

    Article  Google Scholar 

  24. Zhang J, Hu W, Peng T, Yang C (2021) Moving block principle-based multi-strategy optimal scheduling method for trains in case of segment blockages. Sci Sin: Inform 51:413–429

    Article  Google Scholar 

  25. Hajiaghaei-Keshteli M, Fard A (2019) Sustainable closed-loop supply chain network design with discount supposition. Neural Comput Appl 31(9):5343–5377

    Article  Google Scholar 

  26. Keshavarz-Ghorbani F, Pasandideh S (2021) Optimizing a two-level closed-loop supply chain under the vendor managed inventory contract and learning: Fibonacci, ga, iwo, mfo algorithms. Neural Comput Appl 33(15):9425–9450

    Article  Google Scholar 

  27. Zhan S, Zhao J, Peng Q (2016) Real-time train rescheduling on high-speed railway under partial segment blockages. J China Railw Soc 38(10):1–13

    Google Scholar 

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (Grant No. 62273358), the Natural Science Foundation of Hunan Province (Grant No. 2023JJ20075)

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  1. School of Automation, Central South University, Changsha, China

    Han Yang, Wenfeng Hu, Shan Ma & Tao Peng

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  1. Han Yang
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  2. Wenfeng Hu
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  3. Shan Ma
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  4. Tao Peng
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Correspondence to Wenfeng Hu.

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Yang, H., Hu, W., Ma, S. et al. A train dispatching model in case of segment blockages by integrating the prediction of delay propagation. Neural Comput & Applic 36, 3595–3611 (2024). https://doi.org/10.1007/s00521-023-09243-z

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  • DOI: https://doi.org/10.1007/s00521-023-09243-z

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