背景:共享的无码头电动踏板车(e-踏板车)是一种流行的共享移动服务,可在城市和校园环境中提供可访问的最后一英里运输选项。然而,出于安全考虑,城市和校园利益相关者可能会毫不犹豫地介绍这些踏板车。虽然之前的电动踏板车安全研究已经收集了医院的伤害数据或在受控或自然条件下的骑行数据,这些数据集有限,没有发现与电动踏板车骑行安全相关的风险因素.为了解决电动滑板车安全研究中的这一差距,这项研究收集了迄今为止最大的自然电动踏板车数据集,并量化了与行为相关的安全风险,基础设施,和环境因素。
方法:一支由200辆电动踏板车组成的车队部署在布莱克斯堡弗吉尼亚理工大学的校园里,VA为期6个月。五十个配备了独特的机载数据采集系统,使用传感器和视频来完整捕获电动踏板车的行程。由此产生的数据集包括3,500小时的数据,跨越8,500次旅行。开发了算法以识别数据集中的安全关键事件(SCE),并进行了分析以确定各种SCE风险因素的患病率和相关的比值比。
结果:这项研究的结果表明,与基础设施相关的因素,电动滑板车骑手和其他演员的行为,和环境因素都导致了弗吉尼亚理工大学行人密集的校园环境中电动踏板车骑手的SCE风险。
结论:为了帮助减轻不安全的骑手行为,教育推广计划应量化与基础设施相关的重大风险,行为,和环境风险因素,并向骑手提供明确的建议。改进的基础设施维护和设计也可以提高电动踏板车骑手的安全性。
结论:基础设施,行为,本研究中量化的环境风险因素可以由电动踏板车服务提供商应用,市政当局,和校园管理员制定缓解策略,以降低未来与电动踏板车部署相关的安全风险。
Shared dockless electric scooters (e-scooters) are a popular shared mobility service providing an accessible last-mile transportation option in urban and campus environments. However, city and campus stakeholders may hesitate to introduce these scooters due to safety concerns. While prior e-scooter safety studies have collected injury data from hospitals or riding data under controlled or naturalistic conditions, these datasets are limited and did not identify risk factors associated with e-scooter riding safety. To address this gap in e-scooter safety research, this study collected the largest naturalistic e-scooter dataset to date and quantified the safety risks associated with behavioral, infrastructure, and environmental factors.
A fleet of 200 e-scooters was deployed on Virginia Tech\'s campus in Blacksburg, VA for a 6-month period. Fifty were equipped with a unique onboard data acquisition system, using sensors and video to capture e-scooter trips in their entirety. The resulting dataset consisted of 3,500 hours of data spanning over 8,500 trips. Algorithms were developed to identify safety critical events (SCEs) in the dataset and analyses were conducted to determine the prevalence of various SCE risk factors and associated odds ratios.
Results from this study indicate that infrastructure-related factors, behavior of e-scooter riders and other actors, and environmental factors all contributed to the SCE risk for e-scooter riders in Virginia Tech\'s pedestrian-dense campus environment.
To help mitigate unsafe rider behavior, educational outreach programs should quantify the significant risks associated with infrastructure, behavioral, and environmental risk factors and provide clear recommendations to riders. Improved infrastructure maintenance and design may also improve safety for e-scooter riders.
The infrastructure, behavioral, and environmental risk factors quantified in this study can be applied by e-scooter service providers, municipalities, and campus administrators to develop mitigation strategies to reduce the safety risks associated with e-scooter deployments in the future.