Predictive Analytics of Streetcar Bunching Occurrence Time for Real-Time Applications

Abstract

Bunching occurs when transit vehicles are unable to maintain their scheduled headways, resulting in two or more vehicles arriving at a stop in close succession and following each other too closely thereafter. Very few studies have explored the prediction of bunching in real-time, particularly for streetcar services. Predicting the time to bunching in real-time allows transit agencies to take more preventive actions to avoid the occurrence of bunching or to minimize its effects. In this study, we present a comprehensive literature review of the recent research conducted in bunching and real-time prediction models. Based on the findings from the literature review, we propose a model for real-time prediction of streetcar bunching. The Kalman filtering model predicts the travel time to bunching incidents and is tested and analyzed using an automatic vehicle location data feed for one streetcar route (Route 506 Carlton), obtained from the Toronto Transit Commission’s next bus system. The results show that: (1) the model provides good predication quality given that it relies only on the real-time GPS feed of streetcars, which makes it practical for use in real-time prediction applications; (2) the model prediction accuracy improves as the transit vehicle travels away from the terminal; and (3) increasing the number of past days involved in the calculations beyond 6 days or increasing the number of leading trips considered in the same day beyond 7 or 10 trips might increase the prediction error.