Real-time combined safety-mobility assessment using self-driving vehicles collected data
提出一种利用自动驾驶车辆数据实时评估安全与交通运行的方法，通过贝叶斯模型计算碰撞风险和暴露指数，发现服务水平E下平均碰撞风险最高，但考虑暴露时间后，D和E级服务水平下车辆在交叉口和路段遇到极端风险的概率最大。
The study presents a real-time safety and mobility assessment approach using data generated by autonomous vehicles (AVs). The proposed safety assessment method uses Bayesian hierarchical spatial random parameter extreme value model (BHSRP), which can handle the limited availability and uneven distribution of conflict data and accounts for unobserved spatial heterogeneity. The approach estimates two real-time safety metrics: the risk of crash (RC) and return level (RL), using Time-To-Collision (TTC) as conflict indicator. Additionally, a Risk Exposure (RE) index was developed to reflect the risk of an individual vehicle to travel through a corridor. In parallel, the mobility of corridor were assessed based on the highway Capacity manual methodology using real-time traffic data (Highway Capacity Manual, 2010). The study used a 440-hour AVs' dataset of a corridor in Palo Alto, California. After normalizing for each LOS representation in the dataset, LOS E was identified as the most hazardous operating condition with the highest average crash risk. However, the time spent under different operating condition would affect the safety of individual vehicles traveling through a road facility (i.e., vehicle's exposure time). Accounting for exposure time, the vehicle has the highest chance of encountering an extremely risky driving condition at intersections and segments under LOS D and E, respectively.