School of Transportation, Southeast University, China; Jiangsu Key Laboratory of Urban ITS, China; Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, China.
School of Transportation, Southeast University, China; Jiangsu Key Laboratory of Urban ITS, China; Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, China.
Accid Anal Prev. 2021 Jun;156:106124. doi: 10.1016/j.aap.2021.106124. Epub 2021 Apr 16.
Pedestrians are vulnerable when crossing the street, especially at non-signalized crosswalks. In China, in spite of the priority that laws entitle the pedestrians, the yielding rates at non-signalized crosswalks are relatively low. In light of this situation, law enforcement cameras have been used to increase the percentage of drivers yielding to pedestrians. This study investigates the effectiveness of law enforcement cameras on drivers yielding behavior and vehicle-pedestrian conflicts at non-signalized crosswalks. Using Unmanned Aerial Vehicle (UAV) and roadside video recording, information including pedestrian characteristics, vehicular characteristics and environmental factors are collected. The conflict indicators used include Post-Encroachment Time (PET), Time to Collision (TTC), and Deceleration to Safety Time (DST). In this study, a conflict classification framework based on PET, TTC and DST using Support Vector Machine algorithm is employed. A multinomial logit regression model is used to identify the factors contributing to the conflicts. Then, binary logit regression models are constructed to analyze the effects of law enforcement cameras on drivers yielding behavior. Conflict study reveals that the implementation of law enforcement cameras would increase the probability of slight conflict but decrease the probability of serious conflict. Yielding behavior analysis shows that the illegitimate yielding behavior percentages are over 10 %, indicating the necessity of improving the awareness of yielding rules, and the implementation of law enforcement cameras would increase the yielding and legitimate yielding probability. Moreover, factors including the adjacent vehicle yielding behavior, number of lanes between pedestrian and vehicle, pedestrian speed change, pedestrian waiting time, pedestrian accepted gap time, vehicle upstream speed and vehicle speed change are significantly associated with conflict severity and drivers yielding behavior. We recommend that supplementary facilities and measures should be used to improve the safety performance of law enforcement cameras.
行人在过马路时很脆弱，尤其是在没有信号灯的路口。尽管中国法律赋予行人优先权，但在没有信号灯的路口，车辆让行率相对较低。鉴于这种情况，执法摄像头已被用于提高司机礼让行人的比例。本研究调查了执法摄像头对非信号灯路口司机让行行为和车人冲突的有效性。使用无人机 (UAV) 和路边视频记录，收集了包括行人和车辆特征以及环境因素等信息。冲突指标包括侵占后时间 (PET)、碰撞时间 (TTC) 和减速至安全时间 (DST)。本研究采用基于 PET、TTC 和 DST 的支持向量机算法冲突分类框架。采用多项逻辑回归模型来识别导致冲突的因素。然后，构建二项逻辑回归模型来分析执法摄像头对司机让行行为的影响。冲突研究表明，执法摄像头的实施会增加轻微冲突的概率，但会降低严重冲突的概率。让行行为分析表明，非法让行行为的比例超过 10%，这表明有必要提高让行规则的意识，执法摄像头的实施会增加让行和合法让行的概率。此外，相邻车辆让行行为、行人和车辆之间的车道数、行人速度变化、行人等待时间、行人接受间隙时间、车辆上游速度和车辆速度变化等因素与冲突严重程度和司机让行行为显著相关。我们建议应使用补充设施和措施来提高执法摄像头的安全性能。
