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

Jin, S; Qu, X; Wang, D

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

Jin, S Qu, X

Wang, D

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Publication Type:: Journal Article
Citation:: International Journal of Computational Intelligence Systems, 2011, 4 (6), pp. 1122 - 1130
Issue Date:: 2011-12-01

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Field	Value	Language
dc.contributor.author	Jin, S	en_US
dc.contributor.author	Qu, X https://orcid.org/0000-0003-0973-3756	en_US
dc.contributor.author	Wang, D	en_US
dc.date.issued	2011-12-01	en_US
dc.identifier.citation	International Journal of Computational Intelligence Systems, 2011, 4 (6), pp. 1122 - 1130	en_US
dc.identifier.issn	1875-6891	en_US
dc.identifier.uri	http://hdl.handle.net/10453/116513
dc.description.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.	en_US
dc.relation.ispartof	International Journal of Computational Intelligence Systems	en_US
dc.relation.isbasedon	10.1080/18756891.2011.9727860	en_US
dc.title	Assessment of Expressway Traffic Safety Using Gaussian Mixture Model based on Time to Collision	en_US
dc.type	Journal Article
utslib.citation.volume	6	en_US
utslib.citation.volume	4	en_US
utslib.for	089999 Information and Computing Sciences not elsewhere classified	en_US
utslib.for	08 Information and Computing Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CBI - Centre for Built Infrastructure
pubs.organisational-group	/University of Technology Sydney/Strength - CRIN - Realtime Information Networks
utslib.copyright.status	closed_access
pubs.issue	6	en_US
pubs.publication-status	Published	en_US
pubs.volume	4	en_US

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.

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http://hdl.handle.net/10453/116513