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The Safe System Approach (SSA) aims to eliminate fatal and serious injury roadway crashes through a holistic view of the road system, moving away from traditional safety analysis based exclusively on historical crash data. One reason for this is the classification of crashes into broad categories (e.g., head-on, sideswipe), which does not capture crash progression or contributing factors. In this context, this paper applies crash sequence analysis to historical crash data and uses the findings to proactively identify safety issues in similar contexts, in alignment with the SSA framework. The method uses sequence-of-events information from crash data to generate clusters of crashes with similar underlying characteristics. Data from fatal and serious injury crashes from urban intersections in the state of Ohio between 2018 and 2022 were used in the analysis. The results show 12 clusters with unique characteristics that consider the sequence of events of each crash. Although derived from crash data, the clusters offer an in-depth understanding of the factors associated with each one and help identify cluster-specific countermeasures related to various SSA elements. State and local jurisdictions can use the presented methodology in transportation safety programs, by focusing on the clusters that represent local challenges or on countermeasures related to the issues of multiple clusters. Finally, the method can also be associated with site-specific analysis, providing a comprehensive toolkit for practitioners. 

Level 4 automated vehicles (AVs) with the operational design domain (ODD) expanding over time are expected to be the future. Although Level 4 AVs do not require driver takeover, human driving will be necessary outside the ODD. While there is a significant amount of research on takeover/disengagement, no prior studies have explored the safety challenges of manual operation of Level 4 AVs. Crash sequence analysis was employed to compare crashes of the AV (during manual control) (AVM) and general driving population, using U.S. data from California Department of Motor Vehicles crash reports and the Crash Report Sampling System (CRSS) dataset, respectively. Clusters of AVM and CRSS crashes were aggregated into nine groups based on crash context. The results suggest that certain crash groups are more challenging for AVM than for CRSS. AVM crashes are vastly less severe than CRSS crashes for all but one crash group that involved right turns. Nearly half of the AVM crashes involving left and right turns were rear-end crashes, while the majority of similar CRSS crashes were side-swipe or angle. The majority of rear-end AVM crashes occur at intersections, while the converse is true for similar CRSS crashes. Intriguingly, in all the AVM rear-end crashes, the lead vehicle was an AV, suggesting hesitation on the part of the safety driver. For AVM, while lane-changing crashes were less frequent, crashes involving parked vehicles were more frequent than for CRSS. The findings indicate the importance of understanding how driver behavior changes with Level 4 AVs, and how driver training might play an important role in the safety of AVs.