Real-Time Bus Arrival Time Prediction: Case Study for Jinan, China
Publication: Journal of Transportation Engineering
Volume 139, Issue 11
Abstract
Providing real-time bus arrival information can help to improve the service quality of a transit system and enhance its competitiveness among other transportation modes. Taking the city of Jinan, China, as an example, this study proposes two artificial neural network (ANN) models to predict the real-time bus arrivals, based on historical global positioning system (GPS) data and automatic fare collection (AFC) system data. Also, to contend with the difficulty in capturing the traffic fluctuations over different time periods and account for the impact of signalized intersections, this study also subdivides the collected dataset into a bunch of clusters. Sub-ANN models are then developed for each cluster and further integrated into a hierarchical ANN model. To validate the proposed models, six scenarios with respect to different time periods and route lengths are tested. The results reveal that both proposed ANN models can outperform the Kalman filter model. Particularly, with several selected performance indices, it has been found that the hierarchical ANN model clearly outperforms the other two models in most scenarios.
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Acknowledgments
The authors appreciate the GPS data support from Jinan Public Transportation Company, and funding support from the Chinese Council Scholarship.
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Information & Authors
Information
Published In
Journal of Transportation Engineering
Volume 139 • Issue 11 • November 2013
Pages: 1133 - 1140
Copyright
© 2013 American Society of Civil Engineers.
History
Received: Jan 24, 2013
Accepted: Jun 4, 2013
Published online: Jun 6, 2013
Published in print: Nov 1, 2013
Discussion open until: Nov 6, 2013
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