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Dynamic Passenger Assignment for Major Railway Disruptions Considering Information Interventions

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  • Yongqiu Zhu

    (Delft University of Technology)

  • Rob M. P. Goverde

    (Delft University of Technology)

Abstract

Passenger assignment models for major disruptions that require trains to be cancelled/short-turned in railway systems are rarely considered in literature, although these models could make a significant contribution to passenger-oriented disruption timetable design/rescheduling. This paper proposes a dynamic passenger assignment model, where the passengers who start travelling before, during and after the disruption are all considered. The model ensures that on-board passengers are given priority over waiting passengers, and waiting passengers are boarding under the first-come-first-serve rule. Moreover, the model allows information interventions by publishing information about service variations and train congestion at different locations with the aim of distributing passengers wisely to achieve less travel time increase due to the disruption. Discrete event simulation is adopted to implement the model, where loading/unloading procedures are realized and passengers re-plan their paths based on the information they receive. The model tracks individual travels, which helps to evaluate a disruption timetable in a passenger-oriented way.

Suggested Citation

  • Yongqiu Zhu & Rob M. P. Goverde, 2019. "Dynamic Passenger Assignment for Major Railway Disruptions Considering Information Interventions," Networks and Spatial Economics, Springer, vol. 19(4), pages 1249-1279, December.
    • Handle: RePEc:kap:netspa:v:19:y:2019:i:4:d:10.1007_s11067-019-09467-4
    DOI: 10.1007/s11067-019-09467-4
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    References listed on IDEAS

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    Cited by:

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    3. Paulsen, Mads & Rasmussen, Thomas Kjær & Nielsen, Otto Anker, 2021. "Impacts of real-time information levels in public transport: A large-scale case study using an adaptive passenger path choice model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 148(C), pages 155-182.
    4. Xin Zhang & Lei Nie & Xin Wu & Yu Ke, 2020. "How to Optimize Train Stops under Diverse Passenger Demand: a New Line Planning Method for Large-Scale High-Speed Rail Networks," Networks and Spatial Economics, Springer, vol. 20(4), pages 963-988, December.
    5. Zhan, Shuguang & Xie, Jiemin & Wong, S.C. & Zhu, Yongqiu & Corman, Francesco, 2024. "Handling uncertainty in train timetable rescheduling: A review of the literature and future research directions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 183(C).
    6. Khalid Mehmood Alam & Li Xuemei & Saranjam Baig & Li Yadong & Akber Aman Shah, 2020. "Analysis of Technical, Pure Technical and Scale Efficiencies of Pakistan Railways Using Data Envelopment Analysis and Tobit Regression Model," Networks and Spatial Economics, Springer, vol. 20(4), pages 989-1014, December.
    7. Gardner, Clara Brimnes & Nielsen, Sara Dorthea & Eltved, Morten & Rasmussen, Thomas Kjær & Nielsen, Otto Anker & Nielsen, Bo Friis, 2021. "Calculating conditional passenger travel time distributions in mixed schedule- and frequency-based public transport networks using Markov chains," Transportation Research Part B: Methodological, Elsevier, vol. 152(C), pages 1-17.
    8. Sihui Long & Lingyun Meng & Jianrui Miao & Xin Hong & Francesco Corman, 2020. "Synchronizing Last Trains of Urban Rail Transit System to Better Serve Passengers from Late Night Trains of High-Speed Railway Lines," Networks and Spatial Economics, Springer, vol. 20(2), pages 599-633, June.

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