Patterns and predictors of co-occurring risky behaviors among motorcyclists

Motorcyclists, as a vulnerable group of road users, not only suffer from trauma caused by collisions with other vehicles but also from the consequences of their own risky behaviors. As the number of motorcycle crashes has continued to rise in recent years, it is essential to understand trends in motorcyclists' risky behaviors to enhance their safety. This study investigates the co-occurrence of six risky riding behaviors-speeding, alcohol-impaired riding, helmet nonuse, distraction, fatigue, and anger-and identifies rider characteristics associated with distinct behavioral profiles.

This study analyzes data from 4,390 motorcyclists who participated in three annual surveys conducted in Florida between 2021 and 2023. The survey collected information on demographics, riding habits, risky motorcycling behaviors, and the perception that other drivers fail to notice or attend to motorcycles. Descriptive statistics and latent class analysis (LCA) were conducted to identify subgroups of riders based on co-occurring behaviors. A bias-adjusted three-step approach was used to examine associations between class membership and rider characteristics.

Four latent behavioral classes were identified: Very High-risk Riders, Moderate-Risk Riders, Drinking Riders, and Low-risk Riders. Each group exhibited distinct constellations of risky behaviors and demographic or attitudinal profiles. High-risk riders were more likely to be younger, less trained, and not using any safety gear. Alcohol use was especially pronounced among recreational riders regardless of age or gender. Formal motorcycle training was consistently linked to lower-risk classes.

This study advances the understanding of motorcyclist safety by demonstrating how risky behaviors cluster and how specific demographic and perceptual factors distinguish rider subgroups. These findings support the development of comprehensive safety interventions that target subgroups of motorcyclists who share similar risky profiles, rather than treating isolated behaviors.