Identifying high crash risk road segments and accurately predicting crash incidence is fundamental to implementing effective safety countermeasures. While collision data inherently reflects risk, the infrequency and inconsistent reporting of crashes present a major challenge to robust risk prediction models. The proliferation of connected vehicle technology offers a promising avenue to leverage high-density safety metrics for enhanced crash forecasting. A Hard-Braking Event (HBE), interpreted as an evasive maneuver, functions as a potent proxy for elevated driving risk due to its demonstrable correlation with underlying crash causal factors. Crucially, HBE data is significantly more readily available across the entire road network than conventional collision records. This study systematically evaluated the correlation at individual road segment level between police-reported collisions and aggregated and anonymized HBEs identified via the Google Android Auto platform, utilizing datasets from California and Virginia. Empirical evidence revealed that HBEs occur at a rate magnitudes higher than traffic crashes. Employing the state-of-the-practice Negative-Binomial regression models, the analysis established a statistically significant positive correlation between the HBE rate and the crash rate: road segments exhibiting a higher frequency of HBEs were consistently associated with a greater incidence of crashes. This sophisticated model incorporated and controlled for various confounding factors, including road type, speed profile, proximity to ramps, and road segment slope. The HBEs derived from connected vehicle technology thus provide a scalable, high-density safety surrogate metric for network-wide traffic safety assessment, with the potential to optimize safer routing recommendations and inform the strategic deployment of active safety countermeasures.

翻译：识别高风险路段并准确预测事故发生率是实施有效安全对策的基础。虽然碰撞数据本身反映了风险，但事故的低频性和报告不一致性对稳健的风险预测模型构成了重大挑战。网联汽车技术的普及为利用高密度安全指标增强事故预测提供了一条有前景的途径。急刹车事件作为一种规避性操作，因其与潜在事故致因因素存在可论证的关联性，可作为驾驶风险升高的有效代理指标。关键的是，急刹车数据在整个路网中的可获取性显著高于传统的碰撞记录。本研究利用加利福尼亚州和弗吉尼亚州的数据集，系统评估了警方报告的碰撞事件与通过谷歌Android Auto平台识别并经过聚合和匿名化处理的急刹车事件在单个路段层面的相关性。实证证据表明，急刹车事件的发生率比交通事故高出数个数量级。采用当前实践中最先进的负二项回归模型进行分析，研究确立了急刹车率与事故率之间存在统计学上显著的正相关关系：表现出更高急刹车频率的路段始终与更高的事故发生率相关。该复杂模型纳入并控制了多种混杂因素，包括道路类型、速度分布、匝道邻近度以及路段坡度。因此，源自网联汽车技术的急刹车事件为路网范围的交通安全评估提供了一个可扩展的高密度安全代理指标，具有优化安全路线推荐和为主动安全对策的战略部署提供信息的潜力。