Spatial insights into micro-mobility safety: establishing optimal buffers for scooter crash predictions

Abstract

Establishing comparison events/crashes is among the key challenges in safety analysis. This study proposes a spatial consideration for predicting scooter crashes using Utah's five years of crash data. It involves creating buffers ranging from 5 to 250 ft from the point of the scooter crash to obtain comparison crashes. The appropriate variables were selected based on the literature and engineering judgment. The Binary Logistic Regression was then applied to determine the appropriate buffer based on the consistency in the direction and magnitude of the impact of predictor variables. Results indicate that three variables, the junction type, lighting condition, and weather condition, are susceptible to changes in the direction of impact. Moreover, the study findings reveal that as the buffer distance increases, the magnitude of the impact of the variables decreases. Based on the results, a buffer of less than 50 ft is deemed appropriate for various analyses due to consistency in direction and the magnitude of impact. Further, the study findings show that intersections, dark-lighted conditions, summer season, and right-turning movements are more likely to be associated with scooter crashes. These findings can be crucial to transportation agencies and practitioners in improving the safety of scooter riders.

Keywords

Scooter safety / Micro-mobility / Spatial analysis / Optimal buffer