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• Introduces a crash sequence analysis procedure of data processing, sequence encoding, sequence comparison, and clustering. • Compares two event encoding schemes and five optimal-matching-based dissimilarity measures for crash sequence comparison and clustering. • Analyzes single-vehicle crash sequences of events occurred on United States interstate highways in 2016–2018. • Sequence clustering results are evaluated by comparing with a benchmark crash categorization. • Results show that generally, the transition-rate-based localized optimal matching dissimilarity measure and consolidated event encoding scheme generate sequence clustering results closest to the benchmark. Crash sequence analysis has been shown in prior studies to be useful for characterizing crashes and identifying safety countermeasures. Sequence analysis is highly domain-specific, but its various techniques have not been evaluated for adaptation to crash sequences. This paper evaluates the impact of encoding and dissimilarity measures on crash sequence analysis and clustering. Sequence data of interstate highway, single-vehicle crashes in the United States, from 2016 to 2018, were studied. Two encoding schemes and five optimal matching based dissimilarity measures were compared by evaluating the sequence clustering results. The five dissimilarity measures were categorized into two groups based on correlations between dissimilarity matrices. The optimal dissimilarity measure and encoding scheme were identified based on the agreements with a benchmark crash categorization. The transition-rate-based, localized optimal matching dissimilarity and consolidated encoding scheme had the highest agreement with the benchmark. Evaluation results indicate that the selection of dissimilarity measure and encoding scheme determines the results of sequence clustering and crash characterization. A dissimilarity measure that considers the relationships between events and domain context tends to perform well in crash sequence clustering. An encoding scheme that consolidates similar events naturally takes domain context into consideration. [ABSTRACT FROM AUTHOR]

• The conventional approaches of evaluating traffic laws produce unreliable results. • The contiguous-counties design yields robust estimates of various traffic laws. • DUI penalties that restrict future vehicle use reduce traffic fatalities. • Open container and zero-tolerance laws reduce alcohol-induced fatal crashes. In the United States, about 28 lives are lost daily in motor vehicle accidents that involve an alcohol-impaired driver. While most states have enacted various traffic laws to address this phenomenon, little consensus exists on the causal impact of these laws in reducing alcohol-induced fatalities. This paper exploits quasi-random variation in state-level laws to estimate the causal effect of alcohol-related traffic laws on the frequency of fatal accidents. This is identified from the discontinuities in traffic laws among contiguous counties that are separated by a shared state border. We present robust evidence that the conventional approaches that are typically utilized in the literature may erroneously estimate the effectiveness of several alcohol-related laws. [ABSTRACT FROM AUTHOR]

• The paper explores the safety effects of intelligent connected vehicles by penetration rate. • Results from previous 139 analyses are consolidated using meta-analysis. • Safety is enhanced by 4% with the penetration of 10%, and by 43% with 90% MPR. • A case study in the United States is conducted for evaluating crash reduction. • About 13% of crashes can be reduced by intelligent connected vehicles in 2035. As the era of intelligent connected vehicles (ICVs) is approaching, a number of studies have investigated the potential benefits of ICVs, including the safety effects. Although previous studies agree that ICVs would significantly improve traffic safety, its quantified safety effects at different stages are still being debated. This study aims to estimate the ICVs' safety effects by market penetration rate (MPR) adopting a meta-analysis approach. The results from the meta-analysis indicate that the number of conflicts is exponentially reduced as the MPR goes up. For example, compared to the environment without ICVs, 4.2% and 17.4% of conflicts would decrease at the MPR of 10% and 50%, respectively. The effects are more obvious at higher MPR—43.4% of conflicts are expected to decrease at the MPR of 90%. From the case study in the United States based on the meta-analysis, it is expected that the MPR would reach 17–20% in the near future (2025) and 40–48% in 2035. The anticipated reduction in the number of fatal crashes would be 5% and 13%, in 2025 and 2035, respectively. The findings from this study will be useful for both public and private sectors to establish strategic plans to promote ICVs and identify their benefits at different MPRs. [ABSTRACT FROM AUTHOR]