Park Nuri, Park Juneyoung, Joo Yang-Jun, Abdel-Aty Mohamed
Department of Smart City Engineering, Hanyang University, Ansan 15588, South Korea.
Department of Transportation and Logistics Engineering, Smart City Engineering, Hanyang University, Ansan 15588, South Korea.
Accid Anal Prev. 2025 Sep;220:108167. doi: 10.1016/j.aap.2025.108167. Epub 2025 Jul 10.
Traditional approaches to identifying traffic crash hotspots have mainly focused on determining dangerous intersections within road networks, overlooking variations in crash risk within intersections. The micro-level crash hotspot analysis addresses this issue by identifying specific high-risk areas with precision. This study aims to identify micro-level hotspots within three signalized intersections using traffic conflict measures derived from drone video. An algorithm calculates conflicts based on various vehicle sizes and conflict angles. The traffic conflict measures in this study include time-to-collision (TTC), the time to a potential collision assuming constant speed; modified time-to-collision (MTTC), which detects conflicts by assuming constant acceleration; and post-encroachment time (PET), the time gap between two vehicles passing the same point. To select the most appropriate conflict measures and determine optimal thresholds at each intersection, we develop crash frequency models using generalized linear modeling (GLM). These selected conflict measures and thresholds are subsequently used to detect micro-level hotspot sections through kernel density. The results demonstrate that the TTC and PET are strongly related to micro-level crash frequencies, with different patterns emerging depending on crash angle and intersection location. Specifically, TTC-based conflicts are highly correlated with rear-end crashes occurring before the stop line, while PET-based conflicts are closely associated with crashes within the intersection, particularly with left-turning movements. This study contributes to intersection safety by identifying traffic conflict measures for micro-level hotspots and offering detailed safety interventions. These interventions include pavement marking enhancements, stop-line location adjustment, extended left-turn bays, or separated bike lanes, which are based on the specific conflict patterns observed in the study.
传统的识别交通事故热点的方法主要集中在确定道路网络中的危险交叉路口,而忽略了交叉路口内碰撞风险的变化。微观层面的碰撞热点分析通过精确识别特定的高风险区域来解决这一问题。本研究旨在利用从无人机视频中提取的交通冲突指标,识别三个信号控制交叉路口内的微观热点。一种算法根据不同的车辆尺寸和冲突角度计算冲突。本研究中的交通冲突指标包括碰撞时间(TTC),即假设速度恒定情况下的潜在碰撞时间;修正碰撞时间(MTTC),通过假设恒定加速度来检测冲突;以及侵入后时间(PET),即两辆车经过同一点的时间间隔。为了选择最合适的冲突指标并确定每个交叉路口的最佳阈值,我们使用广义线性模型(GLM)开发碰撞频率模型。随后,这些选定的冲突指标和阈值用于通过核密度检测微观热点路段。结果表明,TTC和PET与微观层面的碰撞频率密切相关,根据碰撞角度和交叉路口位置会出现不同的模式。具体而言,基于TTC的冲突与停车线前发生的追尾碰撞高度相关,而基于PET的冲突与交叉路口内的碰撞密切相关,尤其是与左转车辆的碰撞。本研究通过识别微观热点的交通冲突指标并提供详细的安全干预措施,为交叉路口安全做出了贡献。这些干预措施包括改进路面标线、调整停车线位置、延长左转车道或设置独立的自行车道,这些措施是基于研究中观察到的特定冲突模式制定的。