Safety of micro-mobility: Analysis of E-Scooter crashes by mining news reports

• Published in: Accident analysis and prevention Vol. 143; p. 105608
• Main Authors: Yang, Hong, Ma, Qingyu, Wang, Zhenyu, Cai, Qing, Xie, Kun, Yang, Di
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01-08-2020
• Subjects: Crash analysis; Crash data; E-Scooter crashes; Electric scooters; Injury severity; Micro-mobility; Crash analysis; Crash data; Micro-mobility; Electric scooters; E-Scooter crashes; Injury severity
• ISSN: 0001-4575; 1879-2057
• DOI: 10.1016/j.aap.2020.105608

•One of the first studies to quantitatively analyze E-Scooter crash characteristics.•Leveraged massive news reports to collect nationwide E-Scooter crash data.•Analyzed key attributes and their interactions related to E-Scooter crash outcome.•Discussed critical issues related to E-Scooter crash risk and needed efforts.
Dockless electric scooters (E-Scooters) have emerged as a popular micro-mobility mode for urban transportation. This new form of mobility offers riders a flexible option for massive first-/last-mile trips. Despite the popularity, the limited regulations of E-Scooters raise numerous safety concerns among the public and agencies. Due to the unavailability of well-archived crash data, it is difficult to understand and characterize current state quo of E-Scooter-involved crashes. This paper aims to shorten the gap by analyzing a set of reported crash data to describe the patterns of crashes related to E-Scooter use. Specifically, massive media reports were searched and investigated for constructing the crash dataset. Key crash elements such as rider demographics, crash type, and location were organized in an information table for analysis. From 2017 to 2019, there were 169 E-Scooter-involved crashes identified from the news reports across the country. Through the descriptive analysis and cross tabulation analysis, the distinct characteristics of these reported crashes were highlighted. Overall, there was a growing trend for the reported E-Scooter-involved crashes unevenly distributed among the States. The distribution of the crashes across different groups of users, facilities, time periods, and severity levels also showed skewed patterns toward a subset of categories. The quantitative analyses also provide some supportive evidences for warranting the discussion on key issues, including helmet use, riding under influence (RUI), vulnerable riders, and data deficiency. This study highlights the importance of public awareness and timely developing safety countermeasures to mitigate crashes involving E-Scooters.