Please use this identifier to cite or link to this item: https://doi.org/10.1080/18756891.2011.9727860
Title: Assessment of Expressway Traffic Safety Using Gaussian Mixture Model based on Time to Collision
Authors: Jin, S.
Qu, X. 
Wang, D.
Keywords: Expressway traffic safety
Gaussian mixture model
Time to collision
Issue Date: Dec-2011
Source: Jin, S., Qu, X., Wang, D. (2011-12). Assessment of Expressway Traffic Safety Using Gaussian Mixture Model based on Time to Collision. International Journal of Computational Intelligence Systems 4 (6) : 1122-1130. ScholarBank@NUS Repository. https://doi.org/10.1080/18756891.2011.9727860
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. © 2011 Copyright Taylor and Francis Group, LLC.
Source Title: International Journal of Computational Intelligence Systems
URI: http://scholarbank.nus.edu.sg/handle/10635/65181
ISSN: 18756891
DOI: 10.1080/18756891.2011.9727860
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