Rainy weather significantly affects traffic safety, especially in the State of Louisiana. With the aim of identifying the patterns of collective association of attributes in rainfall-involved crashes statewide, crashes that occurred during rainy weather and resulted in two injury groups, fatal and severe injury (FSI) and moderate injury (MI), were extracted from databases acquired from the Louisiana Department of Transportation and Development. A total of 3,381 crashes were extracted, comprising 502 FSI crashes (14.85%) and 2,879 MI crashes (85.15%). This study applied the cluster correspondence analysis (CCA) method, a unique method in combination with cluster analysis and correspondence analysis, to generate clusters by partitioning of individual attributes based on the profiles over the categorical variables identified through dimensional reduction of the dataset. In addition to the biplots illustrating the association of all attributes in the clusters, the top 20 standardized residuals indicating the stronger association are presented in bar plots. Four optimum clusters from FSI and MI crashes reveal that the association of roadway, crash environment, and driver condition characteristics identified in the clusters are highly distinguishable across roadway functional classes. Specifically, varieties of attributes linked to speed limit, lighting condition, alignment, area type, manner of collision, restraint usage, and alcohol/drug can have associative impacts on these two injury severities. The identified associations of crash attributes across various functional class roadways could provide valuable understanding for the development of countermeasures which prioritize the prevention of fatalities and injuries.

In recent years, nonmotorized transportation and micromobility have become increasingly popular in urban areas, and bike sharing and e-scooter usage are more affordable and accessible than ever before. However, users of these modes face several significant challenges, including risky riding behavior and the danger of being involved in collisions or near collisions with other motorists. Because of a lack of relevant data sets, it is difficult to comprehend the underlying causes of e-scooter collisions. This study collected narrative descriptions of 24 e-scooter collisions that occurred in Texas in 2021. The perceptual cycle model (PCM) was applied to these narrative crash reports to analyze the causation patterns of the crash occurrences. The PCM mechanism was applied independently to fatal and injury collisions, and the results were then compared with Haddon’s matrix to develop policy recommendations. The novel approach proposed in this study can be widely utilized by road safety professionals to understand the causal relationships behind other key road safety issues.

As novice teen drivers are uniquely susceptible to the harmful effects of secondary activities on cellphones, 38 states and Washington D.C. have banned all types of cellphone usage for drivers youn...