Highlights The stop-level headway irregularity based on transit smart card data is captured. LS-SVM regression is established to detect bus bunching. More than 95% bus bunching occurrences can be successfully identified. The accuracy does not significantly decay as the prediction lead time increases.
Abstract Bus bunching severely deteriorates the quality of transit service with poor on-time performance and excessive waiting time. To mitigate bus bunching, this paper presents a predictive framework to capture the stop-level headway irregularity based on transit smart card data. Historical headway, passenger demands, and travel time are utilized to model the headway fluctuation at the following stops. A Least Squares Support Vector Machine regression is established to detect bus bunching with the predicted headway pattern. An empirical experiment with two bus routes in Beijing is conducted to demonstrate the effectiveness of the proposed approach. The predictive method can successfully identify more than 95% of bus bunching occurrences in comparison with other well-established prediction algorithms. Moreover, the detection accuracy does not significantly deteriorate as the prediction lead time increases. Instead of regularizing the headways at all costs by adopting certain correction actions, the proposed framework can provide timely and accurate information for potential bus bunching prevention and inform passengers when the next bus will arrive. This feature will greatly increase transit ridership and reduce operating costs for transit authorities.
Headway-based bus bunching prediction using transit smart card data
2016-09-17
15 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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