Estimation of red-light running frequency using high-resolution traffic and signal data.
Red-light-running (RLR) emerges as a major cause that may lead to intersection-related crashes and endanger intersection safety. To reduce RLR violations, it's critical to identify the influential factors associated with RLR and estimate RLR frequency. Without resorting to video camera recordings, this study investigates this important issue by utilizing high-resolution traffic and signal event data collected from loop detectors at five intersections on Trunk Highway 55, Minneapolis, MN. First, a simple method is proposed to identify RLR by fully utilizing the information obtained from stop bar detectors, downstream entrance detectors and advance detectors. Using 12 months of event data, a total of 6550 RLR cases were identified. According to a definition of RLR frequency as the conditional probability of RLR on a certain traffic or signal condition (veh/1000veh), the relationships between RLR frequency and some influential factors including arriving time at advance detector, approaching speed, headway, gap to the preceding vehicle on adjacent lane, cycle length, geometric characteristics and even snowing weather were empirically investigated. Statistical analysis shows good agreement with the traffic engineering practice, e.g., RLR is most likely to occur on weekdays during peak periods under large traffic demands and longer signal cycles, and a total of 95.24% RLR events occurred within the first 1.5s after the onset of red phase. The findings confirmed that vehicles tend to run the red light when they are close to intersection during phase transition, and the vehicles following the leading vehicle with short headways also likely run the red light. Last, a simplified nonlinear regression model is proposed to estimate RLR frequency based on the data from advance detector. The study is expected to helpbetter understand RLR occurrence and further contribute to the future improvement of intersection safety.
School of Transportation Science and Engineering, Beijing Key Laboratory for Cooperative Infrastructure System and Safety Control, Beihang University, Beijing 100191, China. Electronic address: cpeng@buaa.edu.cn.
Peng
P
Chen
School of Transportation Science and Engineering, Beijing Key Laboratory for Cooperative Infrastructure System and Safety Control, Beihang University, Beijing 100191, China. Electronic address: yugz@buaa.edu.cn.
Guizhen
G
Yu
School of Transportation Science and Engineering, Beijing Key Laboratory for Cooperative Infrastructure System and Safety Control, Beihang University, Beijing 100191, China. Electronic address: xinkaiwu@buaa.edu.cn.
Xinkai
X
Wu
School of Transportation Science and Engineering, Beijing Key Laboratory for Cooperative Infrastructure System and Safety Control, Beihang University, Beijing 100191, China. Electronic address: yilong_ren@139.com.
Yilong
Y
Ren
School of Transportation Science and Engineering, Beijing Key Laboratory for Cooperative Infrastructure System and Safety Control, Beihang University, Beijing 100191, China. Electronic address: l224yg@qq.com.
Yueguang
Y
Li
D000063:Accidents, Traffic / Q000706:statistics & numerical data*; D001334:Automobile Driving / Q000331:legislation & jurisprudence / Q000706:statistics & numerical data; D003116:Color; D003617:Dangerous Behavior; D004738:Engineering; D004779:Environment Design; D006801:Humans; D008027:Light; D018986:Motor Vehicles; D011336:Probability; D012449:Safety / Q000706:statistics & numerical data*; D012926:Social Control, Formal; D014741:Video Recording; D014887:Weather
Frequency; High-resolution event data; Influential factors; Loop detector; Red light running; Signalized intersection