  Driving safety analysis has recently experienced unprecedented improvements
thanks to technological advances in precise positioning sensors, artificial
intelligence (AI)-based safety features, autonomous driving systems, connected
vehicles, high-throughput computing, and edge computing servers. Particularly,
deep learning (DL) methods empowered volume video processing to extract
safety-related features from massive videos captured by roadside units (RSU).
Safety metrics are commonly used measures to investigate crashes and
near-conflict events. However, these metrics provide limited insight into the
overall network-level traffic management. On the other hand, some safety
assessment efforts are devoted to processing crash reports and identifying
spatial and temporal patterns of crashes that correlate with road geometry,
traffic volume, and weather conditions. This approach relies merely on crash
reports and ignores the rich information of traffic videos that can help
identify the role of safety violations in crashes. To bridge these two
perspectives, we define a new set of network-level safety metrics (NSM) to
assess the overall safety profile of traffic flow by processing imagery taken
by RSU cameras. Our analysis suggests that NSMs show significant statistical
associations with crash rates. This approach is different than simply
generalizing the results of individual crash analyses, since all vehicles
contribute to calculating NSMs, not only the ones involved in crash incidents.
This perspective considers the traffic flow as a complex dynamic system where
actions of some nodes can propagate through the network and influence the crash
risk for other nodes. We also provide a comprehensive review of surrogate
safety metrics (SSM) in the Appendix A.
