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Risk Riding Behaviors of Urban E-Bikes: A Literature Review

Author

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  • Changxi Ma

    (School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou 730070, China)

  • Dong Yang

    (School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou 730070, China)

  • Jibiao Zhou

    (School of Civil and Transportation Engineering, Ningbo University of Technology, Ningbo 315211, China)

  • Zhongxiang Feng

    (School of Automotive and Traffic Engineering, Hefei University of Technology, Hefei 230009, China)

  • Quan Yuan

    (State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China)

Abstract

In order to clearly understand the risky riding behaviors of electric bicycles (e-bikes) and analyze the riding characteristics, we review the research results of the e-bike risky riding behavior from three aspects: the characteristics and causes of e-bike accidents, the characteristics of users’ traffic behavior, and the prevention and intervention of traffic accidents. The analysis results show that the existing research methods on risky riding behavior of e-bikes mainly involve questionnaire survey methods, structural equation models, and binary probability models. The illegal occupation of motor vehicle lanes, over-speed cycling, red-light running, and illegal manned and reverse cycling are the main risky riding behaviors seen with e-bikes. Due to the difference in physiological and psychological characteristics such as gender, age, audiovisual ability, responsiveness, patience when waiting for a red light, congregation, etc., there are differences in risky cycling behaviors of different users. Accident prevention measures, such as uniform registration of licenses, the implementation of quasi-drive systems, improvements of the riding environment, enhancements of safety awareness and training, are considered effective measures for preventing e-bike accidents and protecting the traffic safety of users. Finally, in view of the shortcomings of the current research, the authors point out three research directions that can be further explored in the future. The strong association rules between risky riding behavior and traffic accidents should be explored using big data analysis. The relationships between risk awareness, risky cycling, and traffic accidents should be studied using the scales of risk perception, risk attitude, and risk tolerance. In a variety of complex mixed scenes, the risk degree, coupling characteristics, interventions, and the coupling effects of various combination intervention measures of e-bike riding behaviors should be researched using coupling theory in the future.

Suggested Citation

  • Changxi Ma & Dong Yang & Jibiao Zhou & Zhongxiang Feng & Quan Yuan, 2019. "Risk Riding Behaviors of Urban E-Bikes: A Literature Review," IJERPH, MDPI, vol. 16(13), pages 1-18, June.
    • Handle: RePEc:gam:jijerp:v:16:y:2019:i:13:p:2308-:d:244102
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    References listed on IDEAS

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    1. Lei Zhang & Shengrui Zhang & Bei Zhou & Yan Huang & Dan Zhao & Shuaiyang Jiao, 2022. "Exploring Unobserved Heterogeneity in Cyclists’ Occupying Motorized Vehicle Lane Behaviors at Different Bike Facility Configurations," IJERPH, MDPI, vol. 19(2), pages 1-22, January.
    2. Jiayu Huang & Ziyi Song & Linlin Xie & Zeting Lin & Liping Li, 2023. "Analysis of Risky Riding Behavior Characteristics of the Related Road Traffic Injuries of Electric Bicycle Riders," IJERPH, MDPI, vol. 20(7), pages 1-12, March.
    3. Piotr Kędziorek & Zbigniew Kasprzyk & Mariusz Rychlicki & Adam Rosiński, 2023. "Analysis and Evaluation of Methods Used in Measuring the Intensity of Bicycle Traffic," Energies, MDPI, vol. 16(2), pages 1-18, January.
    4. Jibiao Zhou & Xinhua Mao & Yiting Wang & Minjie Zhang & Sheng Dong, 2019. "Risk Assessment in Urban Large-Scale Public Spaces Using Dempster-Shafer Theory: An Empirical Study in Ningbo, China," IJERPH, MDPI, vol. 16(16), pages 1-28, August.
    5. Marjolein van der Vlegel & Juanita A. Haagsma & Leonie de Munter & Mariska A. C. de Jongh & Suzanne Polinder, 2020. "Health Care and Productivity Costs of Non-Fatal Traffic Injuries: A Comparison of Road User Types," IJERPH, MDPI, vol. 17(7), pages 1-16, March.
    6. David Kohlrautz & Tobias Kuhnimhof, 2023. "E-Bike Charging Infrastructure in the Workplace—Should Employers Provide It?," Sustainability, MDPI, vol. 15(13), pages 1-11, July.
    7. Jenkins, Michael & Lustosa, Lucio & Chia, Victoria & Wildish, Sarah & Tan, Maria & Hoornweg, Daniel & Lloyd, Meghann & Dogra, Shilpa, 2022. "What do we know about pedal assist E-bikes? A scoping review to inform future directions," Transport Policy, Elsevier, vol. 128(C), pages 25-37.
    8. Luís Pádua & José Sousa & Jakub Vanko & Jonáš Hruška & Telmo Adão & Emanuel Peres & António Sousa & Joaquim J. Sousa, 2020. "Digital Reconstitution of Road Traffic Accidents: A Flexible Methodology Relying on UAV Surveying and Complementary Strategies to Support Multiple Scenarios," IJERPH, MDPI, vol. 17(6), pages 1-24, March.
    9. Anat Meir, 2022. "Can Complete-Novice E-Bike Riders Be Trained to Detect Unmaterialized Traffic Hazards in the Urban Environment? An Exploratory Study," Sustainability, MDPI, vol. 14(17), pages 1-21, August.
    10. van der Waerden, Peter & van der Waerden, Jaap & Gebhard, Sarah, 2024. "Intersection based innovations and cyclists’ route choice decisions in urban areas," Transport Policy, Elsevier, vol. 146(C), pages 205-214.
    11. Tao Wang & Sihong Xie & Xiaofei Ye & Xingchen Yan & Jun Chen & Wenyong Li, 2020. "Analyzing E-Bikers’ Risky Riding Behaviors, Safety Attitudes, Risk Perception, and Riding Confidence with the Structural Equation Model," IJERPH, MDPI, vol. 17(13), pages 1-18, July.
    12. Changxi Ma & Jibiao Zhou & Dong Yang, 2020. "Causation Analysis of Hazardous Material Road Transportation Accidents Based on the Ordered Logit Regression Model," IJERPH, MDPI, vol. 17(4), pages 1-25, February.

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