DocumentCode :
2063309
Title :
A Research about Traffic Safety Character of two-lane highway in Plain Area Basing on Random-effect negative binomial model
Author :
Zhang Tie-jun ; Dan, Liu
Author_Institution :
M.O.C., Beijing, China
Volume :
3
fYear :
2010
fDate :
14-15 Aug. 2010
Firstpage :
385
Lastpage :
389
Abstract :
This paper reviews the studies which have been performed in two-lane highway traffic safety research, and select random-effect negative binomial model to illustrate the traffic safety character of two-lane highway in plain area. More than 1380 kilometers(about 56 highways) two-lane highways in Beijing and Shandong province are investigated and the traffic safety data are collected, such as accident data, geometric data and roadside data. The highways are divided into 1772 segments. The result shows that the random-effect negative binomial model provides a better result than basic model. It is also illustrated that percent of motorcycle in traffic, the percent of truck in traffic, whole driveway density of the highway where the segment is involved, the percent of village length in a highway, driveway density of each sample are associated with higher total accident occurrence, and the width of road surface, the percent of bicycle in traffic tend to point to lower total accident occurrence.
Keywords :
road accidents; road safety; road traffic; traffic engineering computing; Beijing; Shandong province; accident data; geometric data; random-effect negative binomial model; roadside data; traffic safety character; two-lane highway traffic safety research; Accidents; Analytical models; Data models; Predictive models; Roads; Safety; accident prediction mod; highway; random-effect negative binomial model;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Engineering (ICIE), 2010 WASE International Conference on
Conference_Location :
Beidaihe, Hebei
Print_ISBN :
978-1-4244-7506-3
Electronic_ISBN :
978-1-4244-7507-0
Type :
conf
DOI :
10.1109/ICIE.2010.269
Filename :
5571597
Link To Document :
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