Hurricane evacuation, ordered to save lives of people of coastal regions, generates high traffic demand with increased crash risk. To mitigate such risk, transportation agencies need to anticipate highway locations with high crash risks to deploy appropriate countermeasures. With ubiquitous sensors and communication technologies, it is now possible to retrieve micro-level vehicular data containing individual vehicle trajectory and speed information. Such high-resolution vehicle data, potentially available in real time, can be used to assess prevailing traffic safety conditions. Using vehicle speed and acceleration profiles, potential crash risks can be predicted in real time. Previous studies on real-time crash risk prediction mainly used data from infrastructure-based sensors which may not cover many road segments. In this paper, we present methods to determine potential crash risks during hurricane evacuation from an emerging alternative data source known as connected vehicle data. Such data contain vehicle location, speed, and acceleration information collected at a very high frequency (less than 30 seconds). To predict potential crash risks, we utilized a dataset collected during the evacuation period of Hurricane Ida on Interstate-10 (I-10) in the state of Louisiana. Multiple machine learning models were trained considering weather features and different traffic characteristics extracted from the connected vehicle data in 5-minute intervals. The results indicate that the Gaussian Process Boosting (GPBoost) and Extreme Gradient Boosting (XGBoost) models perform better (recall = 0.91) than other models. The real-time connected vehicle data for crash risks assessment will allow traffic managers to efficiently utilize resources to proactively take safety measures.

翻译：为挽救沿海地区居民生命而发布的飓风撤离命令，会产生高交通需求并增加碰撞风险。为降低此类风险，交通管理部门需要预判高碰撞风险的路段，以便部署相应应对措施。借助泛在传感器与通信技术，如今已能获取包含个体车辆轨迹与速度信息的微观级车辆数据。此类高分辨率车辆数据（可实时获取）可用于评估当前交通安全状况。通过利用车辆速度与加速度特征，可实时预测潜在碰撞风险。以往关于实时碰撞风险预测的研究主要基于基础设施传感器数据，但这些传感器无法覆盖全部路段。本文提出一种利用新兴替代数据源（即网联车辆数据）来判定飓风撤离期间潜在碰撞风险的方法。该数据包含以极高频率（低于30秒）采集的车辆位置、速度及加速度信息。为预测潜在碰撞风险，我们采用了飓风"艾达"撤离期间在路易斯安那州10号州际公路（I-10）采集的数据集。研究基于5分钟时间间隔内从网联车辆数据中提取的天气特征与多种交通特性，训练了多个机器学习模型。结果表明，高斯过程提升模型与极端梯度提升模型（召回率=0.91）的预测性能优于其他模型。基于网联车辆数据的实时碰撞风险评估，将有助于交通管理者高效调配资源并主动采取安全措施。