Full Bayesian Method for the Development of Speed Models: Applications of GPS Probe Data
Publication: Journal of Transportation Engineering
Volume 138, Issue 10
Abstract
Traffic speed is one of the basic variables that indicates the level of service of a road entity. It plays an essential role in transportation planning and management. This study attempts to establish a prediction model for speed distribution, in terms of average travel speed and standard deviation, using probe vehicle data in Hong Kong. Taking advantage of detailed traffic flow data obtained from the annual traffic census, a comprehensive traffic information database can be established using the geographical information system technique. The effects of traffic flow, road geometry, and weather conditions on speed distribution are determined using the Markov-chain Monte Carlo (MCMC) simulation approach full Bayesian method.
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Acknowledgments
The work described in this paper was partially supported by a Research Postgraduate Studentship, an Outstanding Researcher Award, the Engineering Postdoctoral Fellowship Programme of the University of Hong Kong, and a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. HKU7176/07E, HKU7188/11E), and a grant from National Natural Science Foundation of China (Grant No. 61021063). We would like to express our sincere thanks to Concord Pacific Satellite Technologies Limited and Motion Power Media Limited for providing the GPS data from the taxis and Transport Department of the HKSAR Government for the traffic flow data from the ATC for this study.
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Information & Authors
Information
Published In
Journal of Transportation Engineering
Volume 138 • Issue 10 • October 2012
Pages: 1188 - 1195
Copyright
© 2012 American Society of Civil Engineers.
History
Received: Jan 26, 2011
Accepted: Mar 15, 2012
Published online: Mar 20, 2012
Published in print: Oct 1, 2012
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