Modeling and Prediction of Bus Operation States for Bunching Analysis
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 146, Issue 9
Abstract
Bus bunching deteriorates transit service quality and passengers’ experience. The modeling and prediction of bus operation states are essential for improving the quality of transit service. Due to the nature of traffic evolution and state transition, bunching-oriented modeling based on bus operation state is more intuitive when compared with the headway-based modeling approach. This work explicitly predicted bus operation state by modeling the dynamic evolution of different states. Five different bus operation states were defined and classified by the K-means algorithm, and the dynamic state evolution was formulated as a Markov chain model. Finally, a multinomial logistic model was developed to predict the bus operation state. A case study was designed to test the performance of the proposed model based on the Global Positioning System (GPS) trajectory data collected from four bus routes in Xi’an, China. The results showed that the proposed model was able to accurately predict the bus operation states.
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Data Availability Statement
Some or all data, models, or code generated or used during the study are available from the corresponding author by request (including the network, GPS trajectories data, and the modeling outputs of the four bus routes).
Acknowledgments
This study is supported by the National Key Research and Development Program of China (No. 2018YFB1600900), the Shaanxi Provincial Science and Technological Project (Grant No. 2020JM-244), and the Science and Technology Project of Department of Transportation in Shaanxi Province (No. 19-24X).
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Information
Published In
Journal of Transportation Engineering, Part A: Systems
Volume 146 • Issue 9 • September 2020
Copyright
© 2020 American Society of Civil Engineers.
History
Received: Oct 9, 2019
Accepted: May 21, 2020
Published online: Jul 14, 2020
Published in print: Sep 1, 2020
Discussion open until: Dec 14, 2020
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