Understanding factors influencing e-scooterist crash risk: A naturalistic study of rental e-scooters in an urban area

In recent years, micromobility has seen unprecedented growth, especially with the introduction of dockless e-scooters. However, the rapid emergence of e-scooters has led to an increase in crashes, resulting in injuries and fatalities, highlighting the need for in-depth analysis to understand the underlying mechanisms. While helpful in quantifying the problem, traditional crash database analysis cannot fully explain the causation mechanisms, e.g., human adaptation failures leading to safety–critical events. Naturalistic data have proven extremely valuable for understanding why crashes happen, but most studies have addressed cars and trucks.

This study is the first to systematically analyze factors contributing to crashes and near-crashes involving rental e-scooters in an urban environment, utilizing naturalistic data. The collected dataset included 6868 trips, covering 9930 km over 709 h with 4694 unique participants. We identified 61 safety–critical events, including 19 crashes and 42 near-crashes, and subsequently labeled variables associated with each event according to the codebook using video data.

Our odds ratio analysis identified that rider experience and behavior (e.g., phone usage, single-handed riding, and pack riding) significantly increase the crash risk. Given the accessibility of rental e-scooters to individuals regardless of their experience, our findings emphasize the need for rider training in addition to education. Influenced by their experience with bicycles, riders may anticipate a similar self-stabilizing mechanism in e-scooters. We found that single-handed riding, which compromises balance, poses a heightened risk, underscoring the crucial role of balance in safe e-scooter operation. Furthermore, the purpose (leisure or commute) and directness (point-to-point or detour) of the trip were also identified as factors influencing the risk, suggesting that user intent plays a role in safety–critical events. Interestingly, our analysis underscores the importance of adapting the crash and near-crash definitions when working with two-wheeled vehicles, especially those in the shared mobility system.