Analysis of potential collisions between pedestrians and personal transportation devices in a university campus: an application of unmanned aerial vehicles

Abstract

Objective To identify factors that contribute to near-miss collisions between pedestrians and personal transportation devices (PTDs) in a university campus using a novel data collection method, unmanned aerial vehicle (UAV). Participants A total of 3,349 pedestrians and 173 PTD riders were detected through UAV observations. Methods The researchers employed UAV technology to capture and geocode the interactions and behavior of pedestrians and PTD riders. Then, a multilevel logistic regression model examined factors that contribute to near-miss collisions between pedestrians and PTDs. Results The model outputs indicate that higher speed, non-bicycle PTDs (eg, skateboard and scooter), and some preventive actions, like reducing speed, deviating, and weaving, increase the probability of a PTD rider getting involved in a near-miss collision. Conclusions Findings can guide campus planners to redesign the streets as a safe environment for all transportation modes and implement appropriate regulations and education programs, especially for non-bicycle PTD riders.