Vulnerable road users (VRUs), such as pedestrians and bicyclists, are at a higher risk of being involved in crashes with motor vehicles, and crashes involving VRUs also are more likely to result in severe injuries or fatalities. Signalized intersections are a major safety concern for VRUs due to their complex and dynamic nature, highlighting the need to understand how these road users interact with motor vehicles and deploy evidence-based countermeasures to improve safety performance. Crashes involving VRUs are relatively infrequent, making it difficult to understand the underlying contributing factors. An alternative is to identify and use conflicts between VRUs and motorized vehicles as a surrogate for safety performance. Automatically detecting these conflicts using a video-based systems is a crucial step in developing smart infrastructure to enhance VRU safety. The Pennsylvania Department of Transportation conducted a study using video-based event monitoring system to assess VRU and motor vehicle interactions at fifteen signalized intersections across Pennsylvania to improve VRU safety performance. This research builds on that study to assess the reliability of automatically generated surrogates in predicting confirmed conflicts using advanced data-driven models. The surrogate data used for analysis include automatically collectable variables such as vehicular and VRU speeds, movements, post-encroachment time, in addition to manually collected variables like signal states, lighting, and weather conditions. The findings highlight the varying importance of specific surrogates in predicting true conflicts, some being more informative than others. The findings can assist transportation agencies to collect the right types of data to help prioritize infrastructure investments, such as bike lanes and crosswalks, and evaluate their effectiveness.

翻译：弱势道路使用者（VRUs），如行人与自行车骑行者，在涉及机动车的碰撞中面临更高风险，且涉及VRUs的碰撞更易导致重伤或死亡。信号交叉口因其复杂多变的特性，成为VRUs的主要安全隐患，凸显出理解这些道路使用者与机动车交互方式以及部署循证对策以提升安全性能的必要性。涉及VRUs的碰撞事故相对稀少，这使得难以理解其潜在致因因素。一种替代方法是识别并利用VRUs与机动化车辆之间的冲突作为安全性能的替代指标。利用基于视频的系统自动检测这些冲突，是开发增强VRUs安全性的智能基础设施的关键一步。宾夕法尼亚州交通局开展了一项研究，使用基于视频的事件监测系统评估全州十五个信号交叉口的VRUs与机动车交互情况，以改善VRUs安全性能。本研究在此基础上，利用先进的数据驱动模型评估自动生成的替代指标在预测确认冲突中的可靠性。用于分析的替代指标数据包括车辆与VRUs速度、运动轨迹、后侵扰时间等自动采集变量，以及信号相位、光照与天气条件等人工采集变量。研究结果揭示了特定替代指标在预测真实冲突中的重要性差异，部分指标信息量更为显著。这些发现可帮助交通机构采集恰当类型的数据，以优先投资自行车道与人行横道等基础设施，并评估其有效性。