Influential factors of red-light running at signalized intersection and prediction using a rare events logistic regression model. (October 2016)

• Record Type: Journal Article
• Authors: Ren, Yilong; Wang, Yunpeng; Wu, Xinkai; Yu, Guizhen; Ding, Chuan
• Is Part Of: Accident analysis and prevention. Volume 95:Part A(2016)
• Page Start: 266
• Page End: 273
• Publication Date: 2016-10
• Subjects: Red light running -- Influential factors -- Rare events -- Logistic regression -- High-resolution traffic data; Accidents -- Prevention -- Periodicals; Accident Prevention -- Periodicals; Accidents -- Prévention -- Périodiques; 363.106
• DOI: 10.1016/j.aap.2016.07.017
• Languages: English
• ISSNs: 0001-4575

Abstract

Highlights: 9-month's Red Light Running (RLR) data were extracted from high-resolution traffic and signal event data to study drivers' RLR behaviors. The significant influential factors which affect drivers' RLR behaviors were identified. A modified rare events logistic regression model was developed for RLR prediction. The new method significantly improves the prediction accuracy and brings great potential for future field applications. Abstract: Red light running (RLR) has become a major safety concern at signalized intersection. To prevent RLR related crashes, it is critical to identify the factors that significantly impact the drivers' behaviors of RLR, and to predict potential RLR in real time. In this research, 9-month's RLR events extracted from high-resolution traffic data collected by loop detectors from three signalized intersections were applied to identify the factors that significantly affect RLR behaviors. The data analysis indicated that occupancy time, time gap, used yellow time, time left to yellow start, whether the preceding vehicle runs through the intersection during yellow, and whether there is a vehicle passing through the intersection on the adjacent lane were significantly factors for RLR behaviors. Furthermore, due to the rare events nature of RLR, a modified rare events logistic regression model was developed for RLR prediction. The rare events logistic regression method has been applied in many fields for rare events studies and shows impressive performance, but so far none of previous research has applied this method to study RLR. The results showed that the rare events logistic regression model performed significantly better than the standard logistic regression model. More importantly, the proposed RLR prediction method is purely based on loop detector data collected from a single advance loop detector located 400 feet away from stop-bar. This brings great potential for future field applications of the proposed method since loops have been widely implemented in many intersections and can collect data in real time. This research is expected to contribute to the improvement of intersection safety significantly.