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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
  • 53 Downloads

Abstract

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.

Keywords

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

JEL Classification

R42 L91 

Notes

Acknowledgements

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.

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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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