E-Scooter safety: The riding risk analysis based on mobile sensing data

Qingyu Ma; Hong Yang; Alan Mayhue; Yunlong Sun; Zhitong Huang; Yifang Ma

doi:10.1016/j.aap.2020.105954

E-Scooter safety: The riding risk analysis based on mobile sensing data

Accid Anal Prev. 2021 Mar:151:105954. doi: 10.1016/j.aap.2020.105954. Epub 2020 Dec 25.

Authors

Qingyu Ma¹, Hong Yang², Alan Mayhue³, Yunlong Sun⁴, Zhitong Huang⁵, Yifang Ma⁶

Affiliations

¹ Department of Computational Modeling and Simulation Engineering, Old Dominion University, Norfolk, VA 23529, USA. Electronic address: qma002@odu.edu.
² Department of Computational Modeling and Simulation Engineering, Old Dominion University, Norfolk, VA 23529, USA. Electronic address: hyang@odu.edu.
³ Department of Computational Modeling and Simulation Engineering, Old Dominion University, Norfolk, VA 23529, USA. Electronic address: amayh002@odu.edu.
⁴ Department of Electrical and Computer Engineering, Old Dominion University, VA 23529, USA. Electronic address: ysun003@odu.edu.
⁵ Surface Transportation Division, Leidos, Inc., Reston, VA 20190, USA. Electronic address: zhitong.huang@leidos.com.
⁶ Department of Information Technology and Decision Sciences, Old Dominion University, Norfolk, VA 23529, USA. Electronic address: yma004@odu.edu.

PMID: 33360874
DOI: 10.1016/j.aap.2020.105954

Abstract

The emergence of shared electric scooter (E-Scooter) systems offers a new micro-mobility mode in many urban areas worldwide. These systems have rapidly attracted numerous trips on various types of facilities such as sidewalks and bike lanes. After their burst of popularity, there are also growing safety concerns about E-Scooter riding. Consequently, a few cities have banned or temporarily suspended E-Scooters as severe crashes occurred. As an emerging micro-mobility mode, its safety performance is significantly understudied as compared to other travel modes such as cars and bicycles. The lack of crash records further prevents it from understanding the underlying mechanisms that drive the occurrences of E-Scooter crashes. The overarching goal of this paper is to probe the safety risk when riding E-Scooters. Specifically, it aims to study the interactions between e-scooter riding and the environment settings through naturalistic riding experiments. Rather than focusing on the analysis of individual riders' heterogeneous behavior (e.g., swinging, hard braking, etc.) and rider characteristics (e.g., age, gender, etc.), the naturalistic riding study examines the riding process in different riding circumstances. A mobile sensing system has been developed to collect data for quantifying the surrogate safety metrics in terms of experienced vibrations, speed changes, and proximity to surrounding objects. The results from naturalistic riding experiments show that E-Scooters can experience notable impacts from different riding facilities. Specifically, compared to bicycle riding, more severe vibration events were associated with E-Scooter riding, regardless of the pavement types. Riding on concrete pavements was found to experience a multiple times higher frequency of vibration events when compared to riding on asphalt pavements of the same length. Riding on both sidewalks and vehicle lanes can both encounter high-frequency close contacts in terms of proximity with other objects. These experimental results suggest that E-Scooters are subject to increased safety challenges due to the increased vibrations, speed variations, and constrained riding environments.

Keywords: E-Scooter safety; Electric scooters; Micro-mobility; Mobile sensing; Naturalistic riding; Riding risk.

MeSH terms

Accidents, Traffic*
Automobiles
Bicycling*
Electricity
Humans
Risk Assessment