Department of Civil, Environmental & Construction Engineering, University of Central Florida, Orlando, FL 32816, USA.
Department of Civil, Environmental & Construction Engineering, University of Central Florida, Orlando, FL 32816, USA.
Accid Anal Prev. 2021 Mar;151:105984. doi: 10.1016/j.aap.2021.105984. Epub 2021 Jan 20.
Safety Performance Functions (SPFs) have been widely used by researchers and practitioners to conduct roadway safety evaluation. Traditional SPFs are usually developed by using annual average daily traffic (AADT) along with geometric characteristics. However, the high level of aggregation may lead to a failure to capture the temporal variation in traffic characteristics (e.g., traffic volume and speed) and crash frequencies. In this study, SPFs at different aggregation levels were developed based on microscopic traffic detector data from California, Florida, and Virginia. More specifically, five aggregation levels were considered: (1) annual average weekday hourly traffic (AAWDHT), (2) annual average weekend hourly traffic (AAWEHT), (3) annual average weekday peak/off-peak traffic (AAWDPT), (4) annual average day of the week traffic (AADOWT), and (5) annual average daily traffic (AADT). Model estimation results showed that the segment length and volume, as exposure variables, are significant across all the aggregation levels. Average speed is significant with a negative coefficient, and the standard deviation of speed was found to be positively associated with the crash frequency. It is noteworthy that the operation of the high occupancy vehicle (HOV) lanes was found to have a positive effect on crash frequency across all the aggregation levels. The model results also showed that the AAWDPT and AADOWT models consistently performed better (the improvements range from 3.14%-16.20%) than the AADT-based SPF, which implies that the differences between the day of the week and peak/off-peak periods should be considered in the development of crash prediction models. The model transferability results indicated that the SPFs between Florida and Virginia are transferrable, while the models between California and the other two states are not transferrable.
安全性能函数 (SPF) 已被研究人员和从业者广泛用于进行道路安全评估。传统的 SPF 通常是使用年平均日交通量 (AADT) 以及几何特征来开发的。然而，这种高度的聚合可能导致无法捕捉交通特征（例如交通量和速度）和碰撞频率的时间变化。在这项研究中，根据来自加利福尼亚州、佛罗里达州和弗吉尼亚州的微观交通探测器数据，开发了不同聚合水平的 SPF。更具体地说，考虑了五个聚合水平：（1）年平均工作日每小时交通量 (AAWHT)，（2）年平均周末每小时交通量 (AAWEHT)，（3）年平均工作日高峰/非高峰交通量 (AAWDPT)，（4）年平均工作日交通量 (AADOWT) 和（5）年平均日交通量 (AADT)。模型估计结果表明，在所有聚合水平下，路段长度和交通量作为暴露变量是显著的。平均速度具有负系数，并且速度的标准差与碰撞频率呈正相关。值得注意的是，发现高乘载车辆 (HOV) 车道的运行对所有聚合水平的碰撞频率都有积极影响。模型结果还表明，AAWDPT 和 AADOWT 模型的性能始终优于基于 AADT 的 SPF（改进幅度在 3.14%-16.20%之间），这意味着在开发碰撞预测模型时应考虑工作日和高峰/非高峰时段之间的差异。模型可转移性结果表明，佛罗里达州和弗吉尼亚州之间的 SPF 是可转移的，而加利福尼亚州和其他两个州之间的模型不可转移。
