Dataset on personal mobility vehicle’s regular riding and fall events

IF 1 Q3 MULTIDISCIPLINARY SCIENCES

Data in Brief Pub Date : 2025-05-19 DOI:10.1016/j.dib.2025.111681

Ramon Sanchez-Iborra , Luis Bernal-Escobedo , Jose Santa

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引用次数: 0

Abstract

Urban environments around the world are being highly populated by personal mobility vehicles, such as scooters or electric bicycles, which offer a new way to move around cities. Researchers from different disciplines are devoting efforts to integrate this novel vehicular paradigm into smart-city ecosystems given its advantages in terms of traffic sustainability, efficiency, and agility. However, the quick penetration of these vehicles also brings challenges and concerns related to their coexistence with other kinds of transportation systems or pedestrians, as well as the high number of accidents in which these vehicles are involved. When an accident happens, a fast and automatic detection is crucial to take quick measures, e.g., alerting emergency services. This is the main motivation of the dataset presented in this work, which provides the data captured by different sensors onboard an electric scooter under regular and accident conditions. A variety of accident kinds such as frontal collisions, lateral falls, etc. are considered, so the dataset may be valuable for the development of automatic engines to infer different riding situations.

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个人机动车辆的常规骑行和跌倒事件数据集

世界各地的城市环境都被小型摩托车或电动自行车等个人移动交通工具所充斥，它们为城市出行提供了一种新的方式。鉴于这种新型交通模式在交通可持续性、效率和敏捷性方面的优势，来自不同学科的研究人员正在努力将其整合到智慧城市生态系统中。然而，这些车辆的快速渗透也带来了与其他交通系统或行人共存的挑战和担忧，以及这些车辆所涉及的大量事故。当事故发生时，快速和自动的检测对于快速采取措施至关重要，例如，向紧急服务部门发出警报。这是本工作中提出的数据集的主要动机，该数据集提供了在常规和事故条件下电动滑板车上不同传感器捕获的数据。考虑了多种事故类型，如正面碰撞、侧面跌落等，因此该数据集可能对自动发动机的开发有价值，可以推断不同的骑行情况。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Data in Brief MULTIDISCIPLINARY SCIENCES-

CiteScore

3.10

自引率

0.00%

发文量

996

审稿时长

70 days

期刊介绍： Data in Brief provides a way for researchers to easily share and reuse each other''s datasets by publishing data articles that: -Thoroughly describe your data, facilitating reproducibility. -Make your data, which is often buried in supplementary material, easier to find. -Increase traffic towards associated research articles and data, leading to more citations. -Open up doors for new collaborations. Because you never know what data will be useful to someone else, Data in Brief welcomes submissions that describe data from all research areas.