Assessment of Expressway Traffic Safety Using Gaussian Mixture Model based on Time to Collision

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Title Assessment of Expressway Traffic Safety Using Gaussian Mixture Model based on Time to Collision
Author Jin, Sheng; Qu, Xiaobo; Wang, Dianhai
Journal Name International Journal of Computational Intelligence Systems
Year Published 2011
Place of publication France
Publisher Atlantis Press
Abstract Traffic safety is of great significance, especially in urban expressway where traffic volume is large and traffic conflicts are highlighted. It is thus important to develop a methodology that is able to assess traffic safety. In this paper, we first analyze the time to collision (TTC) samples from traffic videos collected from Beijing expressway with different locations, lanes, and traffic conditions. Accordingly, some basic descriptive statistics of 5 locations' TTC samples are shown, and it is concluded that Gaussian mixture model (GMM) distribution is the best-fitted distribution to TTC samples based on K-S goodness of fit tests. Using GMM distribution, TTC samples can be divided into three categories: dangerous situations, relative safe situations, and absolute safe situations, respectively. We then proceeds to introduce a novel concept of the percentage of serious traffic conflicts as the percentage of TTC samples below a predetermined threshold value in dangerous situation. After that, assessment results of expressway traffic safety are presented using the proposed traffic safety indictor. The results imply that traffic safety on the weaving segment is lower than that on mainlines and the percentage of serious traffic conflicts on median lane is larger than that on middle lane and shoulder lane.
Peer Reviewed Yes
Published Yes
Alternative URI
Volume 4
Issue Number 6
Page from 1122
Page to 1130
ISSN 1875-6883
Date Accessioned 2012-03-27; 2012-04-15T22:32:02Z
Research Centre Urban Research Program
Faculty Faculty of Science, Environment, Engineering and Technology
Subject Transport Engineering
Publication Type Journal Articles (Refereed Article)
Publication Type Code c1x

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