Full Bayesian Method for the Development of Speed Models: Applications of GPS Probe Data

被引：11
作者：
Pei, Xin [2 ]
Wong, S. C. [1 ]
Li, Y. C. [1 ]
Sze, N. N. [1 ]
机构：
[1] Univ Hong Kong, Dept Civil Engn, Hong Kong, Hong Kong, Peoples R China
[2] Tsinghua Univ, Dept Automat, Beijing 100084, Peoples R China
来源：
JOURNAL OF TRANSPORTATION ENGINEERING | 2012年 / 138卷 / 10期
基金：
中国国家自然科学基金;
关键词：
Traffic speed; Global positioning system; Bayesian analysis; Monte Carlo method; Geometry; Weather conditions; FLOW; RELIABILITY; PERFORMANCE; ACCIDENTS;
D O I：
10.1061/(ASCE)TE.1943-5436.0000428
中图分类号：
TU [建筑科学];
学科分类号：
0813 ;
摘要：
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. DOI: 10.1061/(ASCE)TE.1943-5436.0000428. (C) 2012 American Society of Civil Engineers.
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页码：1188 / 1195
页数：8
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