Public Transport

, Volume 9, Issue 1–2, pp 285–305 | Cite as

Schedule-free high-frequency transit operations

  • Gabriel E. Sánchez-Martínez
  • Nigel H. M. Wilson
  • Haris N. Koutsopoulos
Original Paper


High-frequency transit systems are essential for the socioeconomic and environmental well-being of large and dense cities. The planning and control of their operations are important determinants of service quality. Although headway and optimization-based control strategies generally outperform schedule-adherence strategies, high-frequency operations are mostly planned with schedules, in part because operators must observe resource constraints (neglected by most control strategies) while planning and delivering service. This research develops a schedule-free paradigm for high-frequency transit operations, in which trip sequences and departure times are optimized in real-time, employing stop-skipping strategies and utilizing real-time information to maximize service quality while satisfying operator resource constraints. Following a discussion of possible methodological approaches, a simple methodology is applied to operate a simulated transit service without schedules. Results demonstrate the feasibility of the new paradigm.


High-frequency transit Schedule-free Real-time control 


  1. Abkowitz M, Lepofsky M (1990) Implementing headway-based reliability control on transit routes. J Transp Eng 116(1):49–63CrossRefGoogle Scholar
  2. Adenso-Díaz B, González MO, González-Torre P (1999) On-line timetable re-scheduling in regional train services. Transp Res Part B Methodol 33(6):387–398. doi: 10.1016/S0191-2615(98)00041-1 CrossRefGoogle Scholar
  3. Barnett A (1974) On controlling randomness in transit operations. Transp Sci 8(2):102–116CrossRefGoogle Scholar
  4. Bartholdi JJ, Eisenstein DD (2012) A self-coördinating bus route to resist bus bunching. Transp Res 46B(4):481–491. doi: 10.1016/j.trb.2011.11.001 CrossRefGoogle Scholar
  5. Berrebi SJ, Watkins KE, Laval JA (2015) A real-time bus dispatching policy to minimize passenger wait on a high frequency route. Transp Res Part B Methodol 81:377–389CrossRefGoogle Scholar
  6. Boyle D, Pappas J, Boyle P, Nelson B, Sharfarz D, Benn H (2009) TCRP Report 135: controlling system costs: basic and advanced scheduling manuals and contemporary issues in transit scheduling. Transportation Research Board, Washington, DCCrossRefGoogle Scholar
  7. Cats O, Larijani AN, Koutsopoulos HN, Burghout W (2011) Impacts of holding control strategies on transit performance: bus simulation model analysis. Transp Res Record 2216:51–58CrossRefGoogle Scholar
  8. Ceder A (2007) Public transit planning and operation: theory, modeling and practice. Elsevier, Butterworth-Heinemann, OxfordGoogle Scholar
  9. Corman F, D’Ariano A, Pacciarelli D, Pranzo M (2010) A tabu search algorithm for rerouting trains during rail operations. Transp Res Part B Methodol 44(1):175–192. doi: 10.1016/j.trb.2009.05.004 CrossRefGoogle Scholar
  10. 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(1):79–94. doi: 10.1016/j.trc.2010.09.009 CrossRefGoogle Scholar
  11. Cortés CE, Jara-Díaz S, Tirachini A (2011) Integrating short turning and deadheading in the optimization of transit services. Transp Res Part A Policy Pract 45(5):419–434. doi: 10.1016/j.tra.2011.02.002 CrossRefGoogle Scholar
  12. Daganzo CF, Pilachowski J (2011) Reducing bunching with bus-to-bus cooperation. Transp Res 45B(1):267–277CrossRefGoogle Scholar
  13. D’Ariano A, Pacciarelli D, Pranzo M (2007) A branch and bound algorithm for scheduling trains in a railway network. Eur J Oper Res 183(2):643–657. doi: 10.1016/j.ejor.2006.10.034 CrossRefGoogle Scholar
  14. D’Ariano A, Pacciarelli D, Pranzo M (2008) Assessment of flexible timetables in real-time traffic management of a railway bottleneck. Transp Res Part C Emerg Technol 16(2):232–245. doi: 10.1016/j.trc.2007.07.006 CrossRefGoogle Scholar
  15. Delgado F, Muñoz JC, Giesen R (2012) How much can holding and/or limiting boarding improve transit performance? Transp Res Part B Methodol 46(9):1202–1217CrossRefGoogle Scholar
  16. Desaulniers G, Hickman M (2007) Public transit. In: Barnhart C, Laporte G (eds) Handbooks in OR and MS 14: transportation, pp 69–127Google Scholar
  17. Eberlein XJ, Wilson NH, Bernstein D (2001) The holding problem with real-time information available. Transp Sci 35(1):1–18CrossRefGoogle Scholar
  18. Huisman D (2007) A column generation approach for the rail crew re-scheduling problem. Eur J Oper Res 180(1):163–173. doi: 10.1016/j.ejor.2006.04.026 CrossRefGoogle Scholar
  19. Huisman D, Wagelmans AP (2006) A solution approach for dynamic vehicle and crew scheduling. Eur J Oper Res 172(2):453–471. doi: 10.1016/j.ejor.2004.10.009 CrossRefGoogle Scholar
  20. Kittelson & Associates, Parsons Brinckerhoff, KFH Group, Texas A&M Transportation Institute (2013) TCRP Report 165: Transit Capacity and Quality of Service Manual, 3rd edn. Transportation Research BoardGoogle Scholar
  21. 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(1):62–78. doi: 10.1016/j.trc.2010.12.004 CrossRefGoogle Scholar
  22. Leiva C, Muñoz JC, Giesen R, Larrain H (2010) Design of limited-stop services for an urban bus corridor with capacity constraints. Transp Res Part B Methodol 44(10):1186–1201. doi: 10.1016/j.trb.2010.01.003 CrossRefGoogle Scholar
  23. Mazzarello M, Ottaviani E (2007) A traffic management system for real-time traffic optimisation in railways. Transp Res Part B Methodol 41(2):246–274. doi: 10.1016/j.trb.2006.02.005 CrossRefGoogle Scholar
  24. Mesquita M, Paias A (2008) Set partitioning/covering-based approaches for the integrated vehicle and crew scheduling problem. Comput Oper Res 35(5):1562–1575. doi: 10.1016/j.cor.2006.09.001 CrossRefGoogle Scholar
  25. Osuna EE, Newell GF (1972) Control strategies for an idealized public transportation system. Transp Sci 6(1):52–72CrossRefGoogle Scholar
  26. Rezanova NJ, Ryan DM (2010) The train driver recovery problem—a set partitioning based model and solution method. Comput Oper Res 37(5):845–856. doi: 10.1016/j.cor.2009.03.023 CrossRefGoogle Scholar
  27. Rodriguez J (2007) A constraint programming model for real-time train scheduling at junctions. Transp Res Part B Methodol 41(2):231–245. doi: 10.1016/j.trb.2006.02.006 CrossRefGoogle Scholar
  28. Sáez D, Cortés CE, Milla F, Núñez A, Tirachini A, Riquelme M (2012) Hybrid predictive control strategy for a public transport system with uncertain demand. Transportmetrica 8(1):61–86CrossRefGoogle Scholar
  29. Şahin S (1999) Railway traffic control and train scheduling based oninter-train conflict management. Transp Res Part B Methodol 33(7):511–534. doi: 10.1016/S0191-2615(99)00004-1 CrossRefGoogle Scholar
  30. Sánchez-Martínez GE (2015) Real-time operations planning and control of high-frequency transit. Ph.D. thesis, Massachusetts Institute of TechnologyGoogle Scholar
  31. Sánchez-Martínez G, Koutsopoulos H, Wilson N (2016) Real-time holding control for high-frequency transit with dynamics. Transp Res Part B Methodol 83:1–19. doi: 10.1016/j.trb.2015.11.013 CrossRefGoogle Scholar
  32. Site PD, Filippi F (1998) Service optimization for bus corridors with short-turn strategies and variable vehicle size. Transp Res Part A Policy Pract 32(1):19–38. doi: 10.1016/S0965-8564(97)00016-5 CrossRefGoogle Scholar
  33. Törnquist J, Persson JA (2007) N-tracked railway traffic re-scheduling during disturbances. Transp Res Part B Methodol 41(3):342–362. doi: 10.1016/j.trb.2006.06.001 CrossRefGoogle Scholar
  34. Valouxis C, Housos E (2002) Combined bus and driver scheduling. Comput Oper Res 29(3):243–259. doi: 10.1016/S0305-0548(00)00067-8 CrossRefGoogle Scholar
  35. Veelenturf LP, Potthoff D, Huisman D, Kroon LG (2012) Railway crew rescheduling with retiming. Transp Res Part C Emerg Technol 20(1):95–110. doi: 10.1016/j.trc.2010.09.008 CrossRefGoogle Scholar
  36. Vuchic VR (2005) Urban transit: operations, planning, and economicsGoogle Scholar
  37. Walker CG, Snowdon JN, Ryan DM (2005) Simultaneous disruption recovery of a train timetable and crew roster in real time. Comput Oper Res 32(8):2077–2094. doi: 10.1016/j.cor.2004.02.001 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Gabriel E. Sánchez-Martínez
    • 1
  • Nigel H. M. Wilson
    • 2
  • Haris N. Koutsopoulos
    • 3
  1. 1.CambridgeUSA
  2. 2.CambridgeUSA
  3. 3.BostonUSA

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