Full Bayesian method for the development of speed models: Applications of GPS probe data

Xin Pei; S. C. Wong; Y. C. Li; Nang Ngai Sze

doi:10.1061/(ASCE)TE.1943-5436.0000428

Full Bayesian method for the development of speed models: Applications of GPS probe data

Xin Pei, S. C. Wong, Y. C. Li, Nang Ngai Sze

Research output: Journal article publication › Journal article › Academic research › peer-review

10 Citations (Scopus)

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.

Original language	English
Pages (from-to)	1188-1195
Number of pages	8
Journal	Journal of Transportation Engineering
Volume	138
Issue number	10
DOIs	https://doi.org/10.1061/(ASCE)TE.1943-5436.0000428
Publication status	Published - 1 Oct 2012
Externally published	Yes

Keywords

Bayesian analysis
Geometry
Global positioning system
Monte carlo method
Traffic speed
Weather conditions

ASJC Scopus subject areas

Civil and Structural Engineering
Transportation

More information

10.1061/(ASCE)TE.1943-5436.0000428

Cite this

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AU - Li, Y. C.

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AB - 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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KW - Global positioning system

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KW - Weather conditions

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Full Bayesian method for the development of speed models: Applications of GPS probe data

Abstract

Keywords

ASJC Scopus subject areas

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