Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, P.O.B. 653, Beer-Sheva 84105, Israel.
Department of Industrial Engineering and Management, Ariel University, Ariel 40700, Israel.
Accid Anal Prev. 2016 Nov;96:371-381. doi: 10.1016/j.aap.2015.03.040. Epub 2015 Apr 20.
The yellow signal driver behavior, reflecting the dilemma zone behavior, is analyzed using naturalistic data from digital enforcement cameras. The key variable in the analysis is the entrance time after the yellow onset, and its distribution. This distribution can assist in determining two critical outcomes: the safety outcome related to red-light-running angle accidents, and the efficiency outcome. The connection to other approaches for evaluating the yellow signal driver behavior is also discussed. The dataset was obtained from 37 digital enforcement cameras at non-urban signalized intersections in Israel, over a period of nearly two years. The data contain more than 200 million vehicle entrances, of which 2.3% (∼5million vehicles) entered the intersection during the yellow phase. In all non-urban signalized intersections in Israel the green phase ends with 3s of flashing green, followed by 3s of yellow. In most non-urban signalized roads in Israel the posted speed limit is 90km/h. Our analysis focuses on crossings during the yellow phase and the first 1.5s of the red phase. The analysis method consists of two stages. In the first stage we tested whether the frequency of crossings is constant at the beginning of the yellow phase. We found that the pattern was stable (i.e., the frequencies were constant) at 18 intersections, nearly stable at 13 intersections and unstable at 6 intersections. In addition to the 6 intersections with unstable patterns, two other outlying intersections were excluded from subsequent analysis. Logistic regression models were fitted for each of the remaining 29 intersection. We examined both standard (exponential) logistic regression and four parameters logistic regression. The results show a clear advantage for the former. The estimated parameters show that the time when the frequency of crossing reduces to half ranges from1.7 to 2.3s after yellow onset. The duration of the reduction of the relative frequency from 0.9 to 0.1 ranged from 1.9 to 2.9s.
黄色信号驾驶员行为反映了困境区域行为,利用数字执法摄像机的自然数据进行了分析。分析中的关键变量是黄色信号开始后的进入时间及其分布。该分布有助于确定两个关键结果:与闯红灯事故相关的安全结果和效率结果。还讨论了与评估黄色信号驾驶员行为的其他方法的联系。该数据集是从以色列 37 个非城市信号交叉口的 37 个数字执法摄像机获得的,时间跨度近两年。该数据包含超过 2 亿次车辆入口,其中 2.3%(约 500 万辆车)在黄色时段进入交叉口。在以色列所有非城市信号交叉口,绿色时段以 3 秒闪烁绿色结束,然后是 3 秒黄色。在以色列大多数非城市信号道路上,限速为 90km/h。我们的分析集中在黄色时段和红色时段开始的前 1.5 秒的交叉口。分析方法包括两个阶段。在第一阶段,我们测试了黄色时段开始时的交叉频率是否稳定。我们发现,在 18 个交叉口中,模式是稳定的(即频率是恒定的),在 13 个交叉口中几乎稳定,在 6 个交叉口中不稳定。除了模式不稳定的 6 个交叉口外,还有另外两个异常交叉口被排除在后续分析之外。为剩余的 29 个交叉口中的每一个都拟合了逻辑回归模型。我们检查了标准(指数)逻辑回归和四个参数逻辑回归。结果表明前者具有明显优势。估计的参数表明,频率减半的时间在黄色信号开始后 1.7 到 2.3 秒之间。相对频率从 0.9 减少到 0.1 的持续时间在 1.9 到 2.9 秒之间。