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.

Given the lack of protective structural barriers and advanced restraints, motorcyclists are vulnerable road users. In 2020 in the United States, motorcycle-involved fatalities occurred 28 times more frequently per vehicle mile traveled than passenger car occupant fatalities, causing 5,579 motorcycle-related fatalities—the highest number of motorcyclists killed since 1975. By identifying patterns and relationships between key contributing factors, strategies for reducing motorcycle crashes can be developed. In addition to current efforts, additional research must be conducted using innovative avenues, with increased funding. Bayesian networks can better discover the relationships between potential speed compliance variables. This study used six years (2014 to 2019) of motorcycle crash data in Louisiana to determine the conditional probabilities of the influential factors. In addition to the high contribution of alcohol involvement, two-way undivided roadways, 35 to 44 year-old drivers involved in improper driving behaviors, and crash types are the underlying factors associated with a considerable increase in motorcycle crash severity. The findings of this study can also be used for decision making and strategy development for motorcycle safety.