Full Bayesian Method for the Development of Speed Models: Applications of GPS Probe Data
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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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/oclc/8674831
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Supplemental Notes:
- Copyright © 2012 American Society of Civil Engineers
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Authors:
- Pei, Xin
- Wong, S C
- Li, Y C
- Sze, N N
- Publication Date: 2012-10
Language
- English
Media Info
- Media Type: Web
- Features: Figures; References; Tables;
- Pagination: pp 1188-1195
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Serial:
- Journal of Transportation Engineering
- Volume: 138
- Issue Number: 10
- Publisher: American Society of Civil Engineers
- ISSN: 0733-947X
- Serial URL: https://ascelibrary.org/journal/jtepbs
Subject/Index Terms
- TRT Terms: Bayes' theorem; Geographic information systems; Geometric segments; Global Positioning System; Monte Carlo method; Probe vehicles; Speed distribution; Traffic flow; Traffic speed; Weather conditions
- Geographic Terms: Hong Kong (China)
- Subject Areas: Highways; Operations and Traffic Management; I72: Traffic and Transport Planning;
Filing Info
- Accession Number: 01449062
- Record Type: Publication
- Files: TRIS, ASCE
- Created Date: Oct 10 2012 9:30AM