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A delay estimation technique for single and double-track railroads

Author

Listed:
  • Murali, Pavankumar
  • Dessouky, Maged
  • Ordóñez, Fernando
  • Palmer, Kurt

Abstract

To route and schedule trains over a large complex network can be computationally intensive. One way to reduce complexity could be to "aggregate" suitable sections of a network. In this paper, we present a simulation-based technique to generate delay estimates over track segments as a function of traffic conditions, as well as network topology. We test our technique by comparing the delay estimates obtained for a network in Los Angeles with the delays obtained from the simulation model developed by Lu et al. [Lu, Q., Dessouky, M.M., Leachman, R.C., 2004. Modeling of train movements through complex networks. ACM Transactions on Modeling and Computer Simulation 14, 48-75], which has been shown to be representative of the real-world delay values. Railway dispatchers could route and schedule freight trains over large networks by using our technique to estimate delay across aggregated network sections.

Suggested Citation

  • Murali, Pavankumar & Dessouky, Maged & Ordóñez, Fernando & Palmer, Kurt, 2010. "A delay estimation technique for single and double-track railroads," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 46(4), pages 483-495, July.
    • Handle: RePEc:eee:transe:v:46:y:2010:i:4:p:483-495
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    Citations

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    Cited by:

    1. Thomas Spanninger & Beda Büchel & Francesco Corman, 2023. "Train Delay Predictions Using Markov Chains Based on Process Time Deviations and Elastic State Boundaries," Mathematics, MDPI, vol. 11(4), pages 1-23, February.
    2. Krüger, Niclas A. & Vierth , Inge & Fakhraei Roudsari, Farzad, 2013. "Spatial, temporal and size distribution of freight train delays: evidence from Sweden," Working papers in Transport Economics 2013:8, CTS - Centre for Transport Studies Stockholm (KTH and VTI).
    3. Taslimi, Bijan & Babaie Sarijaloo, Farnaz & Liu, Hongcheng & Pardalos, Panos M., 2022. "A novel mixed integer programming model for freight train travel time estimation," European Journal of Operational Research, Elsevier, vol. 300(2), pages 676-688.
    4. Huang, Ping & Wen, Chao & Fu, Liping & Lessan, Javad & Jiang, Chaozhe & Peng, Qiyuan & Xu, Xinyue, 2020. "Modeling train operation as sequences: A study of delay prediction with operation and weather data," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 141(C).
    5. Krier, Betty & Liu, Chia-Mei & McNamara, Brian & Sharpe, Jerrod, 2014. "Individual freight effects, capacity utilization, and Amtrak service quality," Transportation Research Part A: Policy and Practice, Elsevier, vol. 64(C), pages 163-175.
    6. Eltoukhy, Abdelrahman E.E. & Wang, Z.X. & Chan, Felix T.S. & Fu, X., 2019. "Data analytics in managing aircraft routing and maintenance staffing with price competition by a Stackelberg-Nash game model," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 122(C), pages 143-168.
    7. Leachman, Robert C. & Jula, Payman, 2012. "Estimating flow times for containerized imports from Asia to the United States through the Western rail network," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(1), pages 296-309.
    8. Harshad Khadilkar, 2017. "Data-Enabled Stochastic Modeling for Evaluating Schedule Robustness of Railway Networks," Transportation Science, INFORMS, vol. 51(4), pages 1161-1176, November.

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