Public Transport

, Volume 11, Issue 2, pp 299–320 | Cite as

Understanding the factors that influence the probability and time to streetcar bunching incidents

  • Paula Nguyen
  • Ehab DiabEmail author
  • Amer Shalaby
Original Paper


Bunching is a well-known operational problem for transit agencies and it has negative impacts on service quality and users’ perception. While there has been a substantial amount of literature about understanding the factors associated with bus bunching and strategies used to mitigate the effects of this problem, there has been little research on streetcar bunching. Although bus and streetcar systems share many similarities, one major difference between the two is that streetcars cannot overtake each other. This makes bunching in streetcar networks more critical to the reliability of the system and an important topic that requires more in-depth understanding. This research aims at understanding the factors that are associated with the likelihood of streetcar bunching and to investigate in greater detail the external and internal factors that relate to the time to the initial bunching incident from terminal. To achieve the first goal, the study uses a binary logistic regression model, while it uses an accelerated failure time model to address the second goal. The study utilizes automatic vehicle location system data acquired from the Toronto Transit Commission, the transit provider for the City of Toronto. The models’ results show that headway deviations at terminals are related to both an increase in the probability of bunching and an acceleration of the time to bunching. The discrepancy in vehicle types between two successive streetcars also has the same relationship as headway deviations at terminals. This study offers a better understanding of the factors that are associated with streetcar service bunching, which is an important component of transit service reliability.


Streetcar Bunching Reliability Accelerated failure time (AFT) model Survival analysis 

JEL Classification

R42 L91 



The authors gratefully acknowledge Kenny Ling and Francis Li from the TTC for providing the data used in this paper. This research was funded by the Ontario Research Fund (ORF). The ideas and findings presented in this paper represent the authors’ views in an academic exercise.


  1. An S, Zhang X, Wang J (2015) Finding causes of irregular headways integrating data mining and AHP. ISPRS Int J Geo-Inf 4(4):2604–2618. CrossRefGoogle Scholar
  2. Andres M, Nair R (2017) A predictive-control framework to address bus bunching. Transp Res Part B Methodol 104:123–148. CrossRefGoogle Scholar
  3. Bartholdi J, Eisenstein D (2012) A self-coördinating bus route to resist bus bunching. Transp Res Part B Methodol 46(4):481–491. CrossRefGoogle Scholar
  4. CBC (2013) Will Montreal finally get a modern tram? Retrieved 3 May 2018
  5. Currie G, Shalaby A (2008) Active transit signal priority for streetcars: experience in Melbourne, Australia, and Toronto, Canada. Transp Res Rec 2042:41–49. CrossRefGoogle Scholar
  6. Currie G, Delbosc A, Reynolds J (2012) Modeling dwell time for streetcars in Melbourne, Australia, and Toronto, Canada. Transp Res Rec 2275:22–29. CrossRefGoogle Scholar
  7. Currie G, Delbosc A, Harrison S, Sarvi M (2013) Impact of crowding on streetcar dwell time. Transp Res Rec 2353:100–106. CrossRefGoogle Scholar
  8. Currie G, Burke M, Delbosc A (2014) Performance of Australian Light Rail and comparison with U.S. trends. Transp Res Rec 2419:11–22. CrossRefGoogle Scholar
  9. Daganzo C (2009) A headway-based approach to eliminate bus bunching: systematic analysis and comparisons. Transp Res Part B Methodol 43(10):913–921. CrossRefGoogle Scholar
  10. Daganzo C, Pilachowski J (2011) Reducing bunching with bus-to-bus cooperation. Transp Res Part B Methodol 45(1):267–277. CrossRefGoogle Scholar
  11. Diab E, El-Geneidy A (2015) The farside story: measuring the benefits of bus stop location on transit performance. Transp Res Rec 2538:1–10. CrossRefGoogle Scholar
  12. Diab E, Shalaby A (2018) Subway service down again? Assessing the effects of subway service interruptions on local surface transit performance. Transp Res Rec. Google Scholar
  13. Diab E, Bertini R, El-Geneidy A (2016) Bus transit service reliability: Understanding the impacts of overlapping bus service on headway delays and determinants of bus bunching. In: Paper presented at the the 95th annual meeting of the Transportation Research Board, Washington, DCGoogle Scholar
  14. Feng W, Figliozzi M (2011) Empirical findings of bus bunching distributions and attributes using archived AVL/APC bus data. In: ICCTP 2011: towards sustainable transportation systems—proceedings of the 11th international conference of Chinese transportation professionals. American Society of Civil Engineers (ASCE), pp 4330–4341Google Scholar
  15. Hu W, Shalaby A (2017) Use of automated vehicle location data for route- and segment-level analyses of bus route reliability and speed. Transp Res Rec 2649:9–19. CrossRefGoogle Scholar
  16. Hu W, Diab E, Aboudina A, Shalaby A (2018) The impact of various streetcar types on passenger activity and running times. Transp Res Rec. Google Scholar
  17. Jenkins SP (2008) Survival anaylsis. Institute for Social and Economic Research, University of Essex, ColchesterGoogle Scholar
  18. Kalinowski T (2014) TTC wants to put some POP in your streetcar ride, with all-doors boarding by Jan. 1. Retrieved 3 May 2018
  19. KCRTA (2017) Midtown/UMKC Streetcar Extension Resources—KCRTA. Retrieved 3 May 2018
  20. Kleinbaum D, Klein M (2005) Survival analysis: a self-learning text. Springer Science and Business Media, New YorkGoogle Scholar
  21. Liang S, Zhao S, Lu C, Ma M (2016) A self-adaptive method to equalize headways: numerical analysis and comparison. Transp Res Part B Methodol 87:33–43. CrossRefGoogle Scholar
  22. Ling K, Shalaby A (2005) A reinforcement learning approach to streetcar bunching control. J Intell Transp Syst 9(2):59–68. CrossRefGoogle Scholar
  23. Louie J, Shalaby A, Habib K (2017) Modelling the impact of causal and non-causal factors on disruption duration for Toronto’s subway system: an exploratory investigation using hazard modelling. Accid Anal Prev 98:232–240. CrossRefGoogle Scholar
  24. Mandelzys M, Hellinga B (2010) Identifying causes of performance issues in bus schedule adherence with automatic vehicle location and passenger count data. Transp Res Rec 2143:9–15. CrossRefGoogle Scholar
  25. Mesbah M, Lin J, Currie G (2015) “Weather” transit is reliable? Using AVL data to explore tram performance in Melbourne, Australia. J Traffic Transp Eng (English Edition) 2(3):125–135. CrossRefGoogle Scholar
  26. Moreira-Matias L, Ferreira C, Gama J, Mendes-Moreira J, de Sousa J (2012) Bus bunching detection by mining sequences of headway deviations. Springer, BerlinCrossRefGoogle Scholar
  27. Moreira-Matias L, Cats O, Gama J, Mendes-Moreira J, de Sousa J (2016) An online learning approach to eliminate bus bunching in real-time. Appl Soft Comput 47:460–482. CrossRefGoogle Scholar
  28. Naznin F, Currie G, Logan D (2017) Streetcar safety from tram driver perspective. In: Paper presented at the Transportation Research Board 96th annual meeting, Washington, DCGoogle Scholar
  29. Petit A, Ouyang Y, Lei C (2018) Dynamic bus substitution strategy for bunching intervention. Transp Res Part B Methodol 115:1–16. CrossRefGoogle Scholar
  30. Shalaby A, Abdulhai B, Lee J (2003) Assessment of streetcar transit priority options using microsimulation modelling. Can J Civ Eng 30(6):1000–1009. CrossRefGoogle Scholar
  31. Surprenant-Legault J, El-Geneidy A (2011) Introduction of reserved bus lane: impact on bus running time and on-time performance. Transp Res Rec 2218:10–18. CrossRefGoogle Scholar
  32. TTC (2016a) TTC section one. Retrieved 3 May 2018
  33. TTC (2016b) TTC Service Summary January 3, 2016 to February 13, 2016. Retrieved 3 May 2017
  34. TTC (2018) Toronto Transit Commission CEO’s Report, October 2018. Toronto Transit Commission (TTC), TorontoGoogle Scholar
  35. Verbich D, Diab E, El-Geneidy A (2016) Have they bunched yet? An exploratory study of the impacts of bus bunching on dwell and running times. Public Transp 8(2):225–242. CrossRefGoogle Scholar
  36. Xuan Y, Argote J, Daganzo C (2011) Dynamic bus holding strategies for schedule reliability: optimal linear control and performance analysis. Transp Res Part B Methodol 45(10):1831–1845. CrossRefGoogle Scholar
  37. Yu H, Chen D, Wu Z, Ma X, Wang Y (2016) Headway-based bus bunching prediction using transit smart card data. Transp Res Part C Emerg Technol 72:45–59. CrossRefGoogle Scholar
  38. Yu Z, Wood J, Gayah V (2017) Using survival models to estimate bus travel times and associated uncertainties. Transp Res Part C Emerg Technol 74:366–382. CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Civil and Mineral EngineeringUniversity of TorontoTorontoCanada
  2. 2.Department of Geography and PlanningUniversity of SaskatchewanSaskatoonCanada

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