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Part of the book series: Advances in Industrial Control ((AIC))

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Abstract

In this chapter, background material and literature review on the operation of trains and on urban rail train scheduling are presented. In Sect. 2.1, the operation of trains is introduced, where the automatic train operation (ATO) system is explained in detail. In addition, a brief introduction to fixed block signaling systems and moving block signaling systems is also given. An overview of optimal control approaches for the trajectory planning of a single train and multiple trains is provided in Sect. 2.2. Section 2.3 introduces the urban rail transit scheduling problem is introduced. This chapter concludes with a short summary in Sect. 2.4.

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References

  1. Hansen I, Pachl J (2008) Railway, timetable & traffic: analysis, modelling, simulation. Eurailpress, Hamburg

    Google Scholar 

  2. Midya S, Thottappillil R (2008) An overview of electromagnetic compatibility challenges in European rail traffic management system. Transp Res Part C Emerg Technol 16:515–534

    Article  Google Scholar 

  3. Liu R, Golovicher I (2003) Energy-efficient operation of rail vehicles. Transp Res Part A Policy Pract 37:917–931

    Article  Google Scholar 

  4. Peng H (2008) Urban rail transit system. China Communication Press, Beijing

    Google Scholar 

  5. Dong H, Ning B, Cai B, Hou Z (2010) Automatic train control system development and simulation for high-speed railways. IEEE Circuits Syst Mag 10:6–18

    Article  Google Scholar 

  6. Howlett P, Pudney P (1995) Energy-efficient train control., Advances in industrial controlSpringer, London

    Book  MATH  Google Scholar 

  7. Pontryagin L (1962) The mathematical theory of optimal processes. Interscience publisher, New York

    MATH  Google Scholar 

  8. Franke R, Meyer M, Terwiesch P (2002) Optimal control of the driving of trains. Automatisierungstechnik 50:606–614

    Article  Google Scholar 

  9. Rahn K, Bode C, Albrecht T (2013) Energy-efficient driving in the context of a communications-based train control system (cbtc). In: Proceedings of the 1st IEEE international conference on intelligent rail transportation (2013 IEEE ICIRT). Beijing, China. Paper 152

    Google Scholar 

  10. Mitchell I (2009) The sustainable railway: use of advisory systems for energy savings. IRSE News 151:2–7

    Google Scholar 

  11. Pachl J (2009) Railway operation and control, 2nd edn. Gorham Printing, Centralia

    Google Scholar 

  12. Takeuchi H, Goodman C, Sone S (2003) Moving block signaling dynamics: performance measures and re-starting queued electric trains. IEEE Proc Electr Power Appl 150:483–492

    Article  Google Scholar 

  13. Pearson L (1973) Moving block signalling. Ph.D. thesis, Loughborough University of Technology, Loughborough, UK

    Google Scholar 

  14. Ichikawa K (1968) Application of optimization theory for bounded state variable problems to the operation of a train. Bull Jpn Soc Mech Eng 11:857–865

    Article  Google Scholar 

  15. Howlett P (2000) The optimal control of a train. Ann Oper Re 98:65–87

    Article  MathSciNet  MATH  Google Scholar 

  16. Howlett P, Pudney P, Vu X (2009) Local energy minimization in optimal train control. Automatica 45:2692–2698

    Article  MathSciNet  MATH  Google Scholar 

  17. Vu X (2006) Analysis of necessary conditions for the optimal control of a train. Ph.D. thesis, University of South Australia, Adelaide, Australia

    Google Scholar 

  18. Albrecht A, Howlett P, Pudney P, Vu X (2013) Energy-efficient train control: from local convexity to global optimization and uniqueness. Automatica 49:3072–3078

    Article  MathSciNet  MATH  Google Scholar 

  19. Khmelnitsky E (2000) On an optimal control problem of train operation. IEEE Trans Autom Control 45:1257–1266

    Article  MathSciNet  MATH  Google Scholar 

  20. Ko H, Koseki T, Miyatake M (2004) Application of dynamic programming to optimization of running profile of a train. Computers in railways IX. WIT Press, Southhampton, pp 103–112

    Google Scholar 

  21. Chang C, Xu D (2000) Differential evolution based tuning of fuzzy automatic train operation for mass rapid transit system. IEE Proc Electr Power Appl 147:206–212

    Google Scholar 

  22. Chang C, Sim S (1997) Optimizing train movements through coast control using genetic algorithms. IEE Proc Electr Power Appl 144:65–73

    Google Scholar 

  23. Han S, Byen Y, Baek J, An T, Lee S, Park H (1999) An optimal automatic train operation (ATO) control using genetic algorithms (GA). In: Proceedings of the IEEE region 10 conference (TENCON 99). Cheju Island, South Korea, pp 360–362

    Google Scholar 

  24. Vašak M, Baotić M, Perić N, Bago M (2009) Optimal rail route energy management under constraints and fixed arrival time. In: Proceedings of the European control conference. Budapest, Hungary, pp 2972–2977

    Google Scholar 

  25. Lu Q, Feng X (2011) Optimal control strategy for energy saving in trains under the four-aspected fixed auto block system. J Mod Transp 19:82–87

    Article  MathSciNet  Google Scholar 

  26. Gu Q, Lu X, Tang T (2011) Energy saving for automatic train control in moving block signaling system. In: Proceedings of the 14th international IEEE conference on intelligent transportation systems. DC, USA, Washington, pp 1305–1310

    Google Scholar 

  27. 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 498–502

    Google Scholar 

  28. Ghoseiri K, Szidarovszky F, Asgharpour M (2004) A multi-objective train scheduling model and solution. Transp Res Part B 38:927–952

    Article  MATH  Google Scholar 

  29. IVU.rail (2015) Integrated scheduling, dispatching and optimization

    Google Scholar 

  30. Zhao J, Rahbee A, Wilson NHM (2007) Estimating a rail passenger trip origin-destination matrix using automatic data collection systems. Comput Aided Civ Infrastruct Eng 22:376–387

    Article  Google Scholar 

  31. Elberlein X, Wilson N, Bernstein D (2001) The holding problem with real-time information available. Transp Sci 35:1–18

    Article  MATH  Google Scholar 

  32. Wong S, Tong C (1998) Estimation of time-dependent origindestination matrices for transit networks. Transp Res Part B 32:35–48

    Article  Google Scholar 

  33. Li Y, Cassidy M (2007) A generalized and efficient algorithm for estimating transit route ODs from passenger counts. Transp Res Part B 41:114–125

    Article  Google Scholar 

  34. Cordeau J, Toth P, Vigo D (1998) A survey of optimization models for train routing and scheduling. Transp Sci 32:380–420

    Article  MATH  Google Scholar 

  35. Szpigel B (1972) Optimal train scheduling on a single line railway. In: Proceedings of the international conference on operational research. The Netherlands, Amsterdam, pp 344–351

    Google Scholar 

  36. Petersen E, Taylor A, Martland C (1986) An introduction to computer-assisted train dispatch. J Adv Transp 20:63–72

    Article  Google Scholar 

  37. Kraay D, Harker P, Chen B (1991) Optimal pacing of trains in freight railroads: model formulation and solution. Oper Res 39:82–99

    Article  Google Scholar 

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

    Article  Google Scholar 

  39. Li X, Wang D, Li K, Gao Z (2013) A green train scheduling model and fuzzy multi-objective optimization algorithm. Appl Math Model 37:617–629

    MathSciNet  Google Scholar 

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

    Article  Google Scholar 

  41. Cury J, Gomide F, Mendes MJ (1980) A methodology for generation of optimal schedules for an underground railway systems. IEEE Trans Autom Control 25:217–222

    Article  MATH  Google Scholar 

  42. Assis W, Milani B (2004) Generation of optimal schedules for metro lines using model predictive control. Automatica 40:1397–1404

    Article  MathSciNet  MATH  Google Scholar 

  43. Kwan C, Chang C (2005) Application of evolutionary algorithm on a transportation scheduling problem—the mass rapid transit. In: Proceedings of the IEEE congress on evolutionary computation. Edinburgh, UK, pp 987–994

    Google Scholar 

  44. Liebchen C (2006) Periodic timetable optimization in public Transport. Ph.D. thesis, Technique University of Berlin, Berlin, Germany

    Google Scholar 

  45. Liebchen C (2008) The first optimized railway timetable in practice. Transp Sci 42:420–435

    Article  Google Scholar 

  46. Wong R, Yuen T, Fung K, Leung J (2008) Optimizing timetable synchronization for rail mass transit. Transp Sci 42:57–69

    Article  Google Scholar 

  47. Albrecht T (2009) Automated timetable design for demand-oriented service on suburban railways. Public Transp 1:5–20

    Article  Google Scholar 

  48. Vázquez-Abad F, Zubieta L (2005) Ghost simulation model for the optimization of an urban subway system. Discrete Event Dyn Syst 15:207–235

    Article  MathSciNet  MATH  Google Scholar 

  49. 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 20:79–94

    Article  Google Scholar 

  50. Krasemann JT (2012) Design of an effective algorithm for fast response to the re-scheduling of railway traffic during disturbances. Transp Res Part C Emerg Technol 20:62–78

    Article  Google Scholar 

  51. Dollevoet T, Corman F, D’Ariano A, Huisman D (2012) An iterative optimization framework for delay management and train scheduling. Technical Report EI 2012–10. Erasmus School of Economics (ESE), Rotterdam, The Netherlands

    Google Scholar 

  52. Cacchiani V, Toth P (2009) Nominal and robust train timetabling problems. Eur J Oper Res 219:727–737

    Article  MathSciNet  MATH  Google Scholar 

  53. Khan M, Zhou X (2010) Stochastic optimization model and solution algorithm for robust double-track train-timetabling problem. IEEE Trans Intell Transp Syst 11:81–89

    Article  Google Scholar 

  54. Meng L, Zhou X (2011) Robust single-track train dispatching model under a dynamic and stochastic environment: A scenario-based rolling horizon solution approach. Transp Res Part B Methodological 45:1080–1102

    Article  Google Scholar 

  55. Vansteenwegen P, Oudheusden DV (2006) Developing railway timetables which guarantee a better service. Eur J Oper Res 173:337–350

    Article  MATH  Google Scholar 

  56. Kersbergen B, van den Boom T, De Schutter B (2013) Reducing the time needed to solve the global rescheduling problem for railway networks. In: Proceedings of the 16th international IEEE conference on intelligent transportation systems (ITSC 2013). The Netherlands, The Hague, pp 791–796

    Google Scholar 

  57. Fu L, Liu Q, Calamai P (2003) Real-time optimization model for dynamic scheduling of transit operations. Transp Res Rec 1857:48–55

    Article  Google Scholar 

  58. Ghoneim N, Wirasinghe S (1986) Optimum zone structure during peak periods for existing urban rail lines. Transp Res Part B 20:7–18

    Article  Google Scholar 

  59. Ceder A (1989) Optimal design of transit short-turn trips. Transp Res Rec 1221:8–22

    Google Scholar 

  60. Site P, Filippi F (1998) Service optimization for bus corridors with short-turn strategies and variable vehicle size. Transp Res Part A 32:19–38

    Google Scholar 

  61. Elberlein X, Wilson N, Barnhart C (1998) The real-time deadheading problem in transit operation control. Transp Res Part B 32:77–100

    Article  Google Scholar 

  62. Wong K, Ho T (2007) Dwell-time and run-time control for dc mass rapid transit railways. IET Electr Power Appl 1:956–966

    Article  Google Scholar 

  63. Goodman C, Murata S (2001) Metro traffic regulation from the passenger perspective. Proc Inst Mech Eng Part F: J Rail Rapid Transit 215:137–147

    Article  Google Scholar 

  64. Norio T, Yoshiaki T, Noriyuki T, Chikara H, Kunimitsu M (2005) Train rescheduling algorithm which minimizes passengers’ dissatisfaction. Lecture notes in computer science: innovations in applied artificial intelligence. Springer, Berlin, pp 829–838

    Chapter  Google Scholar 

  65. Freyss M, Giesen R, Muñoz JC (2013) Continuous approximation for skip-stop operation in rail transit. Transp Res Part C 36:419–433

    Article  Google Scholar 

  66. Lee Y (2012) Mathematical modeling for optimizing skip-stop rail transit operation strategy using genetic algorithm. Technical Report. Morgan State University, Department of Transportation and Urban Infrastructure Studies. Baltimore, MD, USA

    Google Scholar 

  67. Elberlein X (1995) Real-time control strategies in transit operations: models and analysis. Ph.D. thesis, Massachusetts Institute of Technology, Cambridge, MA, USA

    Google Scholar 

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Wang, Y., Ning, B., van den Boom, T., De Schutter, B. (2016). Background: Train Operations and Scheduling. In: Optimal Trajectory Planning and Train Scheduling for Urban Rail Transit Systems. Advances in Industrial Control. Springer, Cham. https://doi.org/10.1007/978-3-319-30889-0_2

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  • DOI: https://doi.org/10.1007/978-3-319-30889-0_2

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