What influences crash risk and crash prevalence for e-scootering? Insights from a naturalistic riding study

Artikel i vetenskaplig tidskrift, 2025

Naturalistic data, i.e. data collected in real traffic by road users attending their daily routines, are the gold standard for crash causation analyses. In fact, these data can show the pre-crash road-user behaviour that is hard to observe from other crash data. Naturalistic data from 6868 trips by 4694 distinct participants, collected over a period of 1.5 years from 17 e-scooters, were used to estimate crash risk by means of odds ratios (OR) and crash prevalence by population attributable risk percentage (PARP). We computed OR and PARP, comparing crashes and near-crashes to baseline events from normal riding. The baselines were selected through both matching and random sampling strategies in order to expand and increase the statistical significance of previous results—while also providing new methodological insights for future research on crash causation. This study also investigated the impact of different baseline–to–safety–critical event ratios for the assessment of crash risk. From a safety perspective, our findings suggest that safety interventions that reduce leisure trips, exposure to intersections, trips on Fridays and Saturdays, pack riding, and inexperienced riding should be prioritised. From a methodological perspective, we showed how combining random and matched baselines can help quantify the crash risk and crash prevalence for micromobility vehicles. The results from this study may encourage policymakers to make data-driven decisions regarding e-scooter regulations. Future research should combine data from naturalistic studies and crash databases with data from the perspective of other road users to provide a more holistic view of e-scooter safety.