Identifying the Risk Factors in the Context-of-Use of Electric Kick Scooters Based on a Latent Dirichlet Allocation

Abstract

Accidents related to electric kick scooters, which are widespread globally, are increasing rapidly. However, most of the research on them concentrates on reporting accident status and injury patterns. Therefore, while it is necessary to analyze safety issues from the user's perspective, interviewing or conducting a survey with those involved in an accident may not return enough data due to respondents' memory loss. Therefore, this study aims to identify the risk factors in the context-of-use for electric kick scooters based on a topic modeling method. We collected data on risk episodes involving electric kick scooters experienced by users in their daily lives and applied text mining to analyze text responses describing the risk episodes systematically. A total of 423 risk episodes are collected from 21 electric kick scooter users in South Korea over two months from an online survey. The text responses describing risk episodes were classified into nine topics based on a latent Dirichlet allocation. From the result, four risk factors can be identified by analyzing the derived topics and the cause of the risk according to the context. Moreover, we suggested design improvement directions. This study can be helpful for designing safer electric kick scooters considering safety.