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.

通过使用视频监控系统识别交叉口中的冲突，研究分析表明，对于预测真实冲突，特定的替代数据比其他数据更具信息性，这可以帮助交通部门收集正确类型的数据以评估基础设施投资的优先级和有效性。