Assessment of Expressway Traffic Safety Using Gaussian Mixture Model based on Time to Collision
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 t...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2011-12-01
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| Series: | International Journal of Computational Intelligence Systems |
| Subjects: | |
| Online Access: | https://www.atlantis-press.com/article/2439.pdf |
| _version_ | 1828801685997223936 |
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| author | Sheng Jin Xiaobo Qu Dianhai Wang |
| author_facet | Sheng Jin Xiaobo Qu Dianhai Wang |
| author_sort | Sheng Jin |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-12T06:50:47Z |
| format | Article |
| id | doaj.art-2b743e0e9fa8459696063e40fbf1169e |
| institution | Directory Open Access Journal |
| issn | 1875-6883 |
| language | English |
| last_indexed | 2024-12-12T06:50:47Z |
| publishDate | 2011-12-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj.art-2b743e0e9fa8459696063e40fbf1169e2022-12-22T00:34:04ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832011-12-014610.2991/ijcis.2011.4.6.4Assessment of Expressway Traffic Safety Using Gaussian Mixture Model based on Time to CollisionSheng JinXiaobo QuDianhai WangTraffic 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.https://www.atlantis-press.com/article/2439.pdfTime to collisionGaussian mixture modelExpressway traffic safety. |
| spellingShingle | Sheng Jin Xiaobo Qu Dianhai Wang Assessment of Expressway Traffic Safety Using Gaussian Mixture Model based on Time to Collision International Journal of Computational Intelligence Systems Time to collision Gaussian mixture model Expressway traffic safety. |
| title | Assessment of Expressway Traffic Safety Using Gaussian Mixture Model based on Time to Collision |
| title_full | Assessment of Expressway Traffic Safety Using Gaussian Mixture Model based on Time to Collision |
| title_fullStr | Assessment of Expressway Traffic Safety Using Gaussian Mixture Model based on Time to Collision |
| title_full_unstemmed | Assessment of Expressway Traffic Safety Using Gaussian Mixture Model based on Time to Collision |
| title_short | Assessment of Expressway Traffic Safety Using Gaussian Mixture Model based on Time to Collision |
| title_sort | assessment of expressway traffic safety using gaussian mixture model based on time to collision |
| topic | Time to collision Gaussian mixture model Expressway traffic safety. |
| url | https://www.atlantis-press.com/article/2439.pdf |
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