Analysis of yellow-light running at signalized intersections using high-resolution traffic data |
| |
| Institution: | 1. Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control, School of Transportation Science and Engineering, Beihang University, Beijing 100191, China;2. Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, SiPaiLou #2, Nanjing 210096, China;3. Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor, MI 48109, USA;4. University of Michigan Transportation Research Institute (UMTRI), University of Michigan, Ann Arbor, MI 48109, USA;1. Center for Studies of Hong Kong, Macao and Pearl River Delta, Collaborative Innovation Center for the Cooperation and Development of Hong Kong, Macao and Mainland China, Sun Yat-Sen University, Xingang Xi Road, Guangzhou, China;2. Department of Civil Engineering, National Chi Nan University, No. 1, University Rd, Puli, Nantou County 54561, Taiwan;1. Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, P.O.B. 653, Beer-Sheva 84105, Israel;2. Department of Industrial Engineering and Management, Ariel University, Ariel 40700, Israel;1. Department of Transportation Information and Control Engineering, Tongji University, No. 4800, Cao’an Road, Shanghai 201804, China;2. Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 1538505, Japan;1. Department of Civil Engineering, Lebanese American University, P.O. Box 36, Byblos, Lebanon;2. Department of IT and Operations Management, Lebanese American University, P.O. Box 13-5053, Beirut, Lebanon;3. Department of Clinical Psychology, Psychobiology and Methodology, Faculty of Psychology and Speech Therapy, Universidad de La Laguna, Tenerife, Spain;4. Department of Social Sciences (Psychology), Lebanese American University, P.O. Box 36, Byblos, Lebanon |
| |
| Abstract: | Many accidents occurring at signalized intersections are closely related to drivers’ decisions of running through intersections during yellow light, i.e., yellow-light running (YLR). Therefore it is important to understand the relationships between YLR and the factors which contribute to drivers’ decision of YLR. This requires collecting a large amount of YLR cases. However, existing data collection method, which mainly relies on video cameras, has difficulties to collect a large amount of YLR data. In this research, we propose a method to study drivers’ YLR behaviors using high-resolution event-based data from signal control systems. We used 8 months’ high-resolution data collected by two stop-bar detectors at a signalized intersection located in Minnesota and identified over 30,000 YLR cases. To identify the possible reasons for drivers’ decision of YLR, this research further categorized the YLR cases into four types: “in should-go zone”, “in should-stop zone”, “in dilemma zone”, and “in optional zone” according to the driver’s location when signal turns to yellow. Statistical analysis indicates that the mean values of approaching speed and acceleration rate are significantly different for different types of YLR. We also show that there were about 10% of YLR drivers who cannot run through intersection before traffic light turns to red. Furthermore, based on a strong correlation between hourly traffic volume and number of YLR events, this research developed a regression model that can be used to predict the number of YLR events based on hourly flow rate. This research also showed that snowing weather conditions cause more YLR events. |
| |
| Keywords: | Yellow-light running Signalized intersections High-resolution data Dilemma zone Optional zone |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
