Modeling Drivers’ Stopping Behaviors during Yellow Intervals at Intersections considering Group Heterogeneity
Stopping behavior during yellow intervals is one of the critical driver behaviors correlated with intersection safety. As the main index of stopping behavior, stopping time is typically described by Accelerated Failure Time (AFT) model. In this study, the comparison of survival curves of stopping ti...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2020/8818496 |
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| _version_ | 1832546935936909312 |
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| author | Juan Li Hui Zhang Yanru Zhang Xuan Zhang |
| author_facet | Juan Li Hui Zhang Yanru Zhang Xuan Zhang |
| author_sort | Juan Li |
| collection | DOAJ |
| description | Stopping behavior during yellow intervals is one of the critical driver behaviors correlated with intersection safety. As the main index of stopping behavior, stopping time is typically described by Accelerated Failure Time (AFT) model. In this study, the comparison of survival curves of stopping time confirms the existence of group specific effects on drivers. However, the AFT model is developed based on the homogeneity assumption. To overcome this drawback, shared frailty survival models are developed for stopping time analysis, which consider the group heterogeneity of drivers. The results show that log-logistic based frailty model with age as a grouping variable has the best goodness of fit and prediction accuracy. Analysis of the models’ parameters indicates that phone status, maximum deceleration, vehicles’ speed, and the distance to stopping line at the onset of the yellow signal have significant impacts on stopping time. Additionally, heterogeneity analysis illustrates that young, middle-aged, and female drivers are more likely to brake harshly and stop past the stop line, which may block the intersection. Furthermore, drivers, who are more familiar with traffic environments, are more possible to make reasonable stopping decisions approaching intersections. The results can be utilized by traffic authorities to implement road safety strategies, which will help reduce traffic incidents caused by improper stopping behavior at intersections. |
| format | Article |
| id | doaj-art-40a31669f9cc4b6c988e2225c4c6e641 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-40a31669f9cc4b6c988e2225c4c6e6412025-02-03T06:46:33ZengWileyJournal of Advanced Transportation0197-67292042-31952020-01-01202010.1155/2020/88184968818496Modeling Drivers’ Stopping Behaviors during Yellow Intervals at Intersections considering Group HeterogeneityJuan Li0Hui Zhang1Yanru Zhang2Xuan Zhang3School of Traffic and Transportation, Beijing Jiaotong University, Beijing, ChinaSchool of Traffic and Transportation, Beijing Jiaotong University, Beijing, ChinaJP Morgan Chase, New York, NY, USASam’s Club Technology, Austin, TX, USAStopping behavior during yellow intervals is one of the critical driver behaviors correlated with intersection safety. As the main index of stopping behavior, stopping time is typically described by Accelerated Failure Time (AFT) model. In this study, the comparison of survival curves of stopping time confirms the existence of group specific effects on drivers. However, the AFT model is developed based on the homogeneity assumption. To overcome this drawback, shared frailty survival models are developed for stopping time analysis, which consider the group heterogeneity of drivers. The results show that log-logistic based frailty model with age as a grouping variable has the best goodness of fit and prediction accuracy. Analysis of the models’ parameters indicates that phone status, maximum deceleration, vehicles’ speed, and the distance to stopping line at the onset of the yellow signal have significant impacts on stopping time. Additionally, heterogeneity analysis illustrates that young, middle-aged, and female drivers are more likely to brake harshly and stop past the stop line, which may block the intersection. Furthermore, drivers, who are more familiar with traffic environments, are more possible to make reasonable stopping decisions approaching intersections. The results can be utilized by traffic authorities to implement road safety strategies, which will help reduce traffic incidents caused by improper stopping behavior at intersections.http://dx.doi.org/10.1155/2020/8818496 |
| spellingShingle | Juan Li Hui Zhang Yanru Zhang Xuan Zhang Modeling Drivers’ Stopping Behaviors during Yellow Intervals at Intersections considering Group Heterogeneity Journal of Advanced Transportation |
| title | Modeling Drivers’ Stopping Behaviors during Yellow Intervals at Intersections considering Group Heterogeneity |
| title_full | Modeling Drivers’ Stopping Behaviors during Yellow Intervals at Intersections considering Group Heterogeneity |
| title_fullStr | Modeling Drivers’ Stopping Behaviors during Yellow Intervals at Intersections considering Group Heterogeneity |
| title_full_unstemmed | Modeling Drivers’ Stopping Behaviors during Yellow Intervals at Intersections considering Group Heterogeneity |
| title_short | Modeling Drivers’ Stopping Behaviors during Yellow Intervals at Intersections considering Group Heterogeneity |
| title_sort | modeling drivers stopping behaviors during yellow intervals at intersections considering group heterogeneity |
| url | http://dx.doi.org/10.1155/2020/8818496 |
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