Department of Civil, Environmental, and Construction Engineering, University of Central Florida (UCF), Orlando, FL 32816-2450, United States.
Accid Anal Prev. 2021 Sep;159:106294. doi: 10.1016/j.aap.2021.106294. Epub 2021 Jul 9.
This research develops safety performance functions and identifies the crash hotspots based on estimated vulnerable road users' exposure at intersections and along the roadway segments. The study utilized big data including Automated Traffic Signal Performance Measures (ATSPM) data, crowdsourced data (Strava), Closed Circuit Television (CCTV) surveillance camera videos, crash data, traffic information, roadway features, land use attributes, and socio-demographic characteristics. It comprises an extensive comparison between a wide array of statistical and machine learning models that were developed to estimate pedestrian and bike exposure. The results indicated that the XGBoost approach was the best to estimate vulnerable road users' exposure at intersections as well as bike exposure along the roadway segments. Afterwards, the estimated exposure was utilized as input variables to develop crash prediction models that relate different crash types to potential explanatory variables. Negative Binomial approach was followed to develop crash prediction models to be consistent with the Highway Safety Manual. The results show that the exposure variables (i.e., AADT, bike exposure, and the interaction between them) have significant influences on the two types of crashes (i.e., crashes of vulnerable road users at intersections and bike crashes along the segments). Further, the results indicated that the context classification is significantly related to crashes. Based on the developed models, the PSIs were calculated and the hotspots were identified for the two crash types. It was found that hotspots were more likely to be located near the city of Orlando. Coastal roadways were classified as cold categories regarding bike crashes. Further, C4 roadway segments were found to be significantly related to the increase of vulnerable road users' crashes at intersections and bike crashes along the segments.
本研究基于交叉口和路段上估计的弱势道路使用者暴露度，开发了安全绩效函数并确定了事故热点。该研究利用了大数据，包括自动化交通信号性能指标 (ATSPM) 数据、众包数据（Strava）、闭路电视 (CCTV) 监控摄像头视频、碰撞数据、交通信息、道路特征、土地利用属性和社会人口特征。它对一系列广泛的统计和机器学习模型进行了广泛比较，这些模型是为估计行人和自行车暴露度而开发的。结果表明，XGBoost 方法是估计交叉口弱势道路使用者暴露度以及自行车在路段上暴露度的最佳方法。之后，将估计的暴露度用作输入变量，以开发与不同碰撞类型相关的潜在解释变量的碰撞预测模型。遵循负二项式方法来开发碰撞预测模型，以使模型与公路安全手册保持一致。结果表明，暴露度变量（即 AADT、自行车暴露度以及它们之间的相互作用）对两种类型的碰撞（即交叉口弱势道路使用者碰撞和路段自行车碰撞）有显著影响。此外，结果表明，环境分类与碰撞显著相关。基于开发的模型，计算了 PSI 并确定了两种碰撞类型的热点。结果发现，热点更可能位于奥兰多市附近。沿海道路在自行车碰撞方面被归类为冷类别。此外，发现 C4 道路段与交叉口弱势道路使用者碰撞和路段自行车碰撞的增加显著相关。
