Objective: This study aimed to understand the riding behaviors of electric bike (e-bike) users in Hangzhou after the "Regulations of Zhejiang Province on the Administration of Electric Bicycles".
Methods: The study consisted of two parts, including a questionnaire survey of local e-bike users in Shangcheng District and Jiande County in Hangzhou City, and a cross-sectional observational study of 16 intersections.
Results: A total of 789 e-bike riders participated in the questionnaire survey, and the riding behavior of 99,407 e-bike users was observed. The main purpose of using e-bike was work and daily life, 46.0% of them used e-bikes more than 5 days a week, and 58.5% used e-bikes for less than 30 min each time. A vast majority (81.7%) of e-bike riders believe that the implementation of Zhejiang Regulations has significantly improved the safety level of e-bike riding in the region. The field survey found that the correct rates of helmet wearing by e-bike riders and passengers were 78.83% and 42.27%. The main violations were invalid/non-helmet wearing (21.17%), followed by carrying passengers and running red lights (7.94% and 4.26%). The rates of invalid/non-helmet wearing and running red lights were significantly higher during non-morning rush hour, weekends, and roads without separate non-motorized vehicle lanes than in other conditions (all < 0.05). Additionally, sunny days and crossroads were risk factors for passenger-carrying and invalid/non-helmet wearing compared to rainy/cloudy days and T-intersections.
Conclusions: The phenomenon that e-bike users' correct practice lags far behind the awareness of various violations has shown some improvement. To further enhance safety measures for e-bike riders, it is necessary to promote education, improve infrastructure, and strengthen law enforcement, in support of the "Zhejiang Regulations" and behavioral interventions.
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http://dx.doi.org/10.1016/j.heliyon.2024.e26263 | DOI Listing |
Front Public Health
January 2025
Department of Surgery, University of California, San Francisco, San Francisco, CA, United States.
Background: Shared micromobility programs (SMPs) are integral to urban transport in US cities, providing sustainable transit options. Increased use has raised safety concerns, notably about helmet usage among e-scooter and e-bicycle riders. Prior studies have shown that head and upper extremity injuries have risen with SMP adoption, yet data on helmet use remains sparse.
View Article and Find Full Text PDFPhys Sportsmed
December 2024
Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Objectives: Electric biking (e-biking) is a rapidly growing recreation, sport, and mode of transportation that often presents to emergency departments (EDs) with high-impact head injuries. This study aimed to evaluate the epidemiology of e-bike-related concussions and closed-head injuries (CHI) to inform more effective injury prevention strategies.
Methods: The National Electronic Injury Surveillance (NEISS) was queried for e-bike related concussions and CHIs presented to national EDs from 1 January 2013-31 December 2022.
Accid Anal Prev
November 2024
Country State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle, Hunan University, Changsha, Hunan, 410082, China. Electronic address:
A multi-objective optimization method based on an injury prediction model is proposed to address the increasingly prominent safety issues for e-bike riders in Chinese road traffic. This method aims to enhance the protective effect of vehicle front-end for e-bike riders by encompassing a broader range of test scenarios. Initially, large-scale rider injury response data were collected using automated Madymo simulations.
View Article and Find Full Text PDFAccid Anal Prev
November 2024
Faculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing 100124, PR China. Electronic address:
As electric bikes (e-bikes) rapidly develop in China, their traffic safety issues are becoming increasingly prominent. Accurately detecting risky riding behaviors and conducting mechanism analysis on the multiple risk factors are crucial in formulating and implementing precise management policies. The emergence of shared e-bikes and the advancements in interpretable machine learning present new opportunities for accurately analyzing the determinants of risky riding behaviors.
View Article and Find Full Text PDFInt J Inj Contr Saf Promot
December 2024
College of Transportation Engineering, Chang'an University, Xi'an, China.
This study investigates the impacts of various factors on e-bike riders' injury severity in crashes with motor vehicles, based on the in-vehicle recording video crash data in China. Variables from human factors, vehicle characteristics, road conditions, and environmental attributes are extracted from the video, especially for drivers and riders' illegal and avoidance behaviour before the crash, and sun shade canopy use. Results of mixed logit models reveal that drivers' speeding, running red lights, slow-down and swerve behaviour, light trucks, heavy trucks, and buses have significantly varied impacts on riders' injury.
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