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OD-Dependent Train Scheduling for an Urban Rail Transit Network

  • Yihui WangEmail author
  • Bin Ning
  • Ton van den Boom
  • Bart De Schutter
Chapter
  • 754 Downloads
Part of the Advances in Industrial Control book series (AIC)

Abstract

In the previous two chapters, we have discussed the train schedulingproblem for a single urban rail transit line with OD-independent and OD-dependent passenger demands. In the current chapter, we consider the train scheduling problem for an urban rail transit network, i.e., a collection of interdependent transit lines. An event-driven model is built for train scheduling, which involves three types of events, i.e., departure events , arrival events , and passenger arrival rates change events . The routing of the arriving passengers at transfer stations is formulated in the train scheduling model. Furthermore, the passenger walking time and the passenger transfer process are also taken into account in the train scheduling model. The resulting optimization problem is a real-valued nonlinear nonconvex problem, which can be solved by nonlinear programming approaches and evolution algorithms. The performance and the effectiveness of the event-driven model are evaluated via a case study. This chapter is based on Wang et al. (Transp Res Part C 60: 1–23, 2015) [1].

Keywords

Schedule Period Arrival Event Total Travel Time Sequential Quadratic Programming Method Passenger Flow 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Wang Y, Tang T, Ning B, van den Boom T, De Schutter B (2015) Passenger-demands-oriented train scheduling for an urban rail transit network. Transp Res Part C 60:1–23CrossRefGoogle Scholar
  2. 2.
    Wong S, Tong C (1998) Estimation of time-dependent origindestination matrices for transit networks. Transp Res Part B 32:35–48CrossRefGoogle Scholar
  3. 3.
    Leemis L (1995) Reliability: probabilistic models and statistical methods. Prentice Hall, Englewood CliffszbMATHGoogle Scholar
  4. 4.
    Bocharnikov Y, Tobias A, Roberts C, Hillmansen S, Goodman C (2007) Optimal driving strategy for traction energy saving on DC suburban railways. IEE Proc: Electr Power Appl 1:675–682Google Scholar
  5. 5.
    Ding Y, Bai Y, Liu F, Mao B (2009) Simulation algorithm for energy-efficient train control under moving block system. In: Proceedings of the 2009 World congress on computer science and information engineering, Los Angeles, CA, USA, pp 498–502Google Scholar
  6. 6.
    Yang X, Li X, Gao Z, Wang H, Tang T (2013) A cooperative scheduling model for timetable optimization in subway systems. IEEE Transp Intell Transp Syst 14:438–447CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Yihui Wang
    • 1
    Email author
  • Bin Ning
    • 1
  • Ton van den Boom
    • 2
  • Bart De Schutter
    • 2
  1. 1.State Key Laboratory of Rail Traffic Control and SafetyBeijing Jiaotong UniversityBeijingChina
  2. 2.Delft Center for Systems and ControlDelft University of TechnologyDelftThe Netherlands

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