Analysis of yellow-light running at signalized intersections using high-resolution traffic data: Fid-Bau Portal

Analysis of yellow-light running at signalized intersections using high-resolution traffic data

Lu, Guangquan / Wang, Yunpeng / Wu, Xinkai / Liu, Henry X.

Highlights • Drivers’ YLR behavior is analyzed using high-resolution event-based data. • The YLR cases have been categorized into four types. • The YLR drivers’ approaching speed and acceleration have been analyzed. • A regression model is developed to estimate the number of YLR based on flow rate. • The impact of snowing weather on YLR events is investigated.

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 8months’ 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.