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

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.

[1]  Robert L. Bertini,et al.  Bus transit service reliability: Understanding the impacts of overlapping bus service on headway delays and determinants of bus bunching , 2016 .

[2]  Vikash V. Gayah,et al.  Using survival models to estimate bus travel times and associated uncertainties , 2017 .

[3]  Graham Currie,et al.  Active transit signal priority for streetcars: experience in Melbourne and Toronto , 2008 .

[4]  Graham Currie,et al.  Active Transit Signal Priority for Streetcars , 2008 .

[5]  Carlos F. Daganzo,et al.  Reducing bunching with bus-to-bus cooperation , 2011 .

[6]  Kenny Ling,et al.  A Reinforcement Learning Approach to Streetcar Bunching Control , 2005, J. Intell. Transp. Syst..

[7]  Matthias Andres,et al.  A predictive-control framework to address bus bunching , 2017 .

[8]  Khandker Nurul Habib,et al.  Modelling the impact of causal and non-causal factors on disruption duration for Toronto's subway system: An exploratory investigation using hazard modelling. , 2017, Accident; analysis and prevention.

[9]  Carlos F. Daganzo,et al.  A headway-based approach to eliminate bus bunching: Systematic analysis and comparisons , 2009 .

[10]  Bruce Hellinga,et al.  Identifying Causes of Performance Issues in Bus Schedule Adherence with Automatic Vehicle Location and Passenger Count Data , 2010 .

[11]  Amer Shalaby,et al.  Use of Automated Vehicle Location Data for Route- and Segment-Level Analyses of Bus Route Reliability and Speed , 2017 .

[12]  Ahmed M El-Geneidy,et al.  Introduction of Reserved Bus Lane , 2011 .

[13]  Oded Cats,et al.  An online learning approach to eliminate Bus Bunching in real-time , 2016, Appl. Soft Comput..

[14]  Yanfeng Ouyang,et al.  Dynamic bus substitution strategy for bunching intervention , 2018, Transportation Research Part B: Methodological.

[15]  Donald D. Eisenstein,et al.  A self-coördinating bus route to resist bus bunching , 2012 .

[16]  Aya Aboudina,et al.  The Impact of Various Streetcar Types on Passenger Activity and Running Times , 2018, Transportation Research Record: Journal of the Transportation Research Board.

[17]  D. Kleinbaum,et al.  Survival Analysis: A Self-Learning Text. , 1996 .

[18]  Baher Abdulhai,et al.  Assessment of streetcar transit priority options using microsimulation modelling , 2003 .

[19]  Minghui Ma,et al.  A self-adaptive method to equalize headways: Numerical analysis and comparison , 2016 .

[20]  Miguel A. Figliozzi,et al.  Empirical Findings of Bus Bunching Distributions and Attributes Using Archived AVL/APC Bus Data , 2011 .

[21]  Graham Currie,et al.  Impact of Crowding on Streetcar Dwell Time , 2013 .

[22]  Graham Currie,et al.  Success and Challenges in Modernizing Streetcar Systems , 2007 .

[23]  Carlos F. Daganzo,et al.  Dynamic bus holding strategies for schedule reliability: Optimal linear control and performance analysis , 2011 .

[24]  Ahmed El-Geneidy,et al.  Have they bunched yet? An exploratory study of the impacts of bus bunching on dwell and running times , 2016, Public Transp..

[25]  Graham Currie,et al.  Modeling Dwell Time for Streetcars in Melbourne, Australia, and Toronto, Canada , 2012 .

[26]  João Mendes-Moreira,et al.  Bus Bunching Detection : A Sequence Mining Approach , 2013 .

[27]  G. Currie,et al.  Performance of Australian Light Rail and Comparison with U.S. Trends , 2014 .

[28]  Amer Shalaby,et al.  Subway Service Down Again? Assessing the Effects of Subway Service Interruptions on Local Surface Transit Performance , 2018, Transportation Research Record: Journal of the Transportation Research Board.

[29]  Yunpeng Wang,et al.  Headway-based bus bunching prediction using transit smart card data , 2016 .

[30]  Erik Kjems,et al.  Transportation Research Record , 2016 .

[31]  Ahmed M El-Geneidy,et al.  The far side story: Measuring the benefits of bus stop location on transit performance , 2015 .

[32]  Carlos Abreu Ferreira,et al.  Bus Bunching Detection by Mining Sequences of Headway Deviations , 2012, ICDM.

[33]  Graham Currie,et al.  Streetcar Safety from Tram Driver Perspective , 2017 .

[34]  G. Currie,et al.  “Weather” transit is reliable? Using AVL data to explore tram performance in Melbourne, Australia , 2015 .

[35]  Jian Wang,et al.  Finding Causes of Irregular Headways Integrating Data Mining and AHP , 2015, ISPRS Int. J. Geo Inf..