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Locating optimal timetables and vehicle schedules in a transit line

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  • Juan Mesa
  • Francisco Ortega
  • Miguel Pozo

Abstract

This paper deals with the Transit Network Timetabling and Scheduling Problem (TNTSP) in a public transit line. The TNTSP aims at determining optimal timetables for each line in a transit network by establishing departure and arrival times of each vehicle at each station. We assume that customers know departure times of line runs offered by the system. However, each user, traveling after or before than their desired travel time, will give rise to an inconvenience cost, or a penalty cost if that user cannot be served according to the scheduled timetable. The provided formulation allocates each user to the best possible timetable considering capacity constraints. The problem is formulated using a p-median based approach and solved using a clustering technique. Computational results that show useful applications of this methodology are also included. Copyright Springer Science+Business Media New York 2014

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  • Juan Mesa & Francisco Ortega & Miguel Pozo, 2014. "Locating optimal timetables and vehicle schedules in a transit line," Annals of Operations Research, Springer, vol. 222(1), pages 439-455, November.
    • Handle: RePEc:spr:annopr:v:222:y:2014:i:1:p:439-455:10.1007/s10479-013-1393-5
    DOI: 10.1007/s10479-013-1393-5
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    3. Yin, Jiateng & D’Ariano, Andrea & Wang, Yihui & Yang, Lixing & Tang, Tao, 2021. "Timetable coordination in a rail transit network with time-dependent passenger demand," European Journal of Operational Research, Elsevier, vol. 295(1), pages 183-202.
    4. Laporte, Gilbert & Ortega, Francisco A. & Pozo, Miguel A. & Puerto, Justo, 2017. "Multi-objective integration of timetables, vehicle schedules and user routings in a transit network," Transportation Research Part B: Methodological, Elsevier, vol. 98(C), pages 94-112.
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    6. Gang Chen & Liping Jiang, 2016. "Managing customer arrivals with time windows: a case of truck arrivals at a congested container terminal," Annals of Operations Research, Springer, vol. 244(2), pages 349-365, September.

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