New Automatic Incident Detection Algorithm Based on Traffic Data Collected for Journey Time Estimation
Publication: Journal of Transportation Engineering
Volume 139, Issue 8
Abstract
A new automatic incident detection algorithm based on the available data originally collected for journey time estimation in Hong Kong is proposed in this paper. Instead of installing a greater number of expensive detectors, the proposed algorithm has proved feasible in effective traffic incident detection, with the available data collected by both video traffic detectors and automatic vehicle identification readers. The proposed algorithm extends the previous standard normal deviate algorithm in the aspects of mathematical model, input data, and detection logic. Two new traffic parameters are proposed as indicators of incidents. They are the coefficient of variation of speed at the upstream detector and the correlation coefficient of speeds of two adjacent detectors. Historical traffic and accident data on an urban road in Hong Kong are used for calibration and validation of the proposed algorithm. This proposed algorithm outperforms five existing algorithms based on the available data for journey time estimation in Hong Kong. It is expected that the proposed algorithm could be used for incident detection in cities even when data are collected only for journey time estimation.
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Acknowledgments
This research is sponsored by a postgraduate stipend, a competitive earmarked research grant from the Research Grant Council of the Hong Kong Special Administrative Region (HKSAR) (Project No. PolyU 5196/10E). The authors also would like to thank the Transport Dept. of the HKSAR for providing TRADS and JTIS data from 2009 to 2010. The three anonymous reviewers are also acknowledged for their valuable comments in improving the quality of this paper.
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Information & Authors
Information
Published In
Journal of Transportation Engineering
Volume 139 • Issue 8 • August 2013
Pages: 840 - 847
Copyright
© 2013 American Society of Civil Engineers.
History
Received: Aug 16, 2012
Accepted: Apr 8, 2013
Published online: Jul 15, 2013
Published in print: Aug 1, 2013
Discussion open until: Dec 15, 2013
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