IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i3p799-d311752.html
   My bibliography  Save this article

Research on the Relationship between the Individual Characteristics of Electric Bike Riders and Illegal Speeding Behavior: A Questionnaire-Based Study

Author

Listed:
  • Changxi Ma

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

  • Jibiao Zhou

    (College of Transportation Engineering, Tongji University, Shanghai 200092, China
    Intelligent Transport System (ITS) R & D Center, Shanghai Urban Construction Design and Research Institute (Group) Co., Ltd., Shanghai 200125, China)

  • Dong Yang

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

  • Yuanyuan Fan

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

Abstract

To examine the relationship between electric bike riders’ individual characteristics and their riding speed, this paper obtained 350 valid survey responses from e-bike riders using an on-site sampling survey method. Using the non-aggregate theory, we take the individual attributes of the rider’s age, driving age, personality, and corrective vision as potential influencing factors. The metric model of the influencing factors of the rider’s personal characteristics on riding speed is established, and we analyze the sensitivity of many influencing factors by using the theory of elasticity. The results show that the absolute value of the elasticity value corresponding to the rider’s gender, age, corrected visual acuity, and other factors is less than 1, which indicates that the above factors have no flexibility regarding the rider’s riding speed selection behavior. However, in four selection intervals, the elasticity values of the rider’s education level are 1.577, 2.484, 1.810, and 1.667; those of their driving age are −1.537, −2.061, −1.547, and −1.606, and those of their riding proficiency are 3.302, 12.038, 10.370, and 11.177, which indicate that the three factors of rider’s education level, driving age, and riding proficiency have a significant impact on the riding speed choice behavior. The finding of the study is helpful for the relevant government departments to formulate more accurate classified intervention measures, and effectively prevent the occurrence of illegal speeding behavior.

Suggested Citation

  • Changxi Ma & Jibiao Zhou & Dong Yang & Yuanyuan Fan, 2020. "Research on the Relationship between the Individual Characteristics of Electric Bike Riders and Illegal Speeding Behavior: A Questionnaire-Based Study," Sustainability, MDPI, vol. 12(3), pages 1-12, January.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:3:p:799-:d:311752
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/3/799/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/12/3/799/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Bai, Lu & Liu, Pan & Chan, Ching-Yao & Li, Zhibin, 2017. "Estimating level of service of mid-block bicycle lanes considering mixed traffic flow," Transportation Research Part A: Policy and Practice, Elsevier, vol. 101(C), pages 203-217.
    2. Cherry, Christopher R. & Yang, Hongtai & Jones, Luke R. & He, Min, 2016. "Dynamics of electric bike ownership and use in Kunming, China," Transport Policy, Elsevier, vol. 45(C), pages 127-135.
    3. Cherry, Christopher & Cervero, Robert, 2007. "Use characteristics and mode choice behavior of electric bike users in China," Transport Policy, Elsevier, vol. 14(3), pages 247-257, May.
    4. Shen, Qing & Chen, Peng & Pan, Haixiao, 2016. "Factors affecting car ownership and mode choice in rail transit-supported suburbs of a large Chinese city," Transportation Research Part A: Policy and Practice, Elsevier, vol. 94(C), pages 31-44.
    5. Changxi Ma & Ruichun He & Wei Zhang, 2018. "Path optimization of taxi carpooling," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-15, August.
    6. Yanyong Guo & Yao Wu & Jian Lu & Jibiao Zhou, 2019. "Modeling the Unobserved Heterogeneity in E-bike Collision Severity Using Full Bayesian Random Parameters Multinomial Logit Regression," Sustainability, MDPI, vol. 11(7), pages 1-12, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lidong Zhu & Mujahid Ali & Elżbieta Macioszek & Mahdi Aghaabbasi & Amin Jan, 2022. "Approaching Sustainable Bike-Sharing Development: A Systematic Review of the Influence of Built Environment Features on Bike-Sharing Ridership," Sustainability, MDPI, vol. 14(10), pages 1-17, May.
    2. Zhixue Li & Zhongxiang Huang & Jie Wang, 2022. "Association of Illegal Motorcyclist Behaviors and Injury Severity in Urban Motorcycle Crashes," Sustainability, MDPI, vol. 14(21), pages 1-11, October.
    3. Jibiao Zhou & Tao Zheng & Sheng Dong & Xinhua Mao & Changxi Ma, 2022. "Impact of Helmet-Wearing Policy on E-Bike Safety Riding Behavior: A Bivariate Ordered Probit Analysis in Ningbo, China," IJERPH, MDPI, vol. 19(5), pages 1-21, February.
    4. Mallikarjun Patil & Bandhan Bandhu Majumdar & Prasanta Kumar Sahu & Long T. Truong, 2021. "Evaluation of Prospective Users’ Choice Decision toward Electric Two-Wheelers Using a Stated Preference Survey: An Indian Perspective," Sustainability, MDPI, vol. 13(6), pages 1-22, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Ziwen Ling & Christopher R. Cherry & John H. MacArthur & Jonathan X. Weinert, 2017. "Differences of Cycling Experiences and Perceptions between E-Bike and Bicycle Users in the United States," Sustainability, MDPI, vol. 9(9), pages 1-18, September.
    3. Ton, Danique & Duives, Dorine, 2021. "Understanding long-term changes in commuter mode use of a pilot featuring free e-bike trials," Transport Policy, Elsevier, vol. 105(C), pages 134-144.
    4. Tomasz Bieliński & Łukasz Dopierała & Maciej Tarkowski & Agnieszka Ważna, 2020. "Lessons from Implementing a Metropolitan Electric Bike Sharing System," Energies, MDPI, vol. 13(23), pages 1-21, November.
    5. Liu, Yixiao & Tian, Zihao & Pan, Baoran & Zhang, Wenbin & Liu, Yunqi & Tian, Lixin, 2022. "A hybrid big-data-based and tolerance-based method to estimate environmental benefits of electric bike sharing," Applied Energy, Elsevier, vol. 315(C).
    6. Nematchoua, ModesteKameni & Deuse, Caroline & Cools, Mario & Reiter, Sigrid, 2020. "Evaluation of the potential of classic and electric bicycle commuting as an impetus for the transition towards environmentally sustainable cities: A case study of the university campuses in Liege, Bel," Renewable and Sustainable Energy Reviews, Elsevier, vol. 119(C).
    7. Li, Xintong & Han, Chunyang & Huang, Helai & Pervez, Amjad & Xu, Guangming & Hu, Cheng & Jiang, Qianshan & Wei, Yulu, 2023. "Pursuing higher acceptability and compliance for electric two-wheeler standardization policy in China: The importance of socio-demographic characteristics, psychological factors, and travel habits," Transportation Research Part A: Policy and Practice, Elsevier, vol. 167(C).
    8. Mathijs Haas & Maarten Kroesen & Caspar Chorus & Sascha Hoogendoorn-Lanser & Serge Hoogendoorn, 2022. "E-bike user groups and substitution effects: evidence from longitudinal travel data in the Netherlands," Transportation, Springer, vol. 49(3), pages 815-840, June.
    9. Tang, Tie-Qiao & Luo, Xiao-Feng & Zhang, Jian & Chen, Liang, 2018. "Modeling electric bicycle’s lane-changing and retrograde behaviors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 1377-1386.
    10. Genikomsakis, Konstantinos N. & Galatoulas, Nikolaos-Fivos & Ioakimidis, Christos S., 2021. "Towards the development of a hotel-based e-bike rental service: Results from a stated preference survey and techno-economic analysis," Energy, Elsevier, vol. 215(PA).
    11. Ou, Hui & Tang, Tie-Qiao & Rui, Ying-Xu & Zhou, Jie-Ming, 2018. "Electric bicycle management and control at a signalized intersection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 1000-1008.
    12. de Kruijf, Joost & van der Waerden, Peter & Feng, Tao & Böcker, Lars & van Lierop, Dea & Ettema, Dick & Dijst, Martin, 2021. "Integrated weather effects on e-cycling in daily commuting: A longitudinal evaluation of weather effects on e-cycling in the Netherlands," Transportation Research Part A: Policy and Practice, Elsevier, vol. 148(C), pages 305-315.
    13. Dong, Hongming & Zhong, Shiquan & Xu, Shuxian & Tian, Junfang & Feng, Zhongxiang, 2021. "The relationships between traffic enforcement, personal norms and aggressive driving behaviors among normal e-bike riders and food delivery e-bike riders," Transport Policy, Elsevier, vol. 114(C), pages 138-146.
    14. Sun, Shichao & Yao, Yukun & Xu, Lingyu & He, Xuan & Duan, Zhengyu, 2022. "The use of E-moped increases commute satisfaction and subjective well-being: Evidence from Shanghai, China," Transport Policy, Elsevier, vol. 117(C), pages 60-73.
    15. Lin, Xiao & Wells, Peter & Sovacool, Benjamin K., 2018. "The death of a transport regime? The future of electric bicycles and transportation pathways for sustainable mobility in China," Technological Forecasting and Social Change, Elsevier, vol. 132(C), pages 255-267.
    16. Yide Liu & Ivan Ka Wai Lai, 2020. "The Effects of Environmental Policy and the Perception of Electric Motorcycles on the Acceptance of Electric Motorcycles: An Empirical Study in Macau," SAGE Open, , vol. 10(1), pages 21582440198, January.
    17. Khashayar Kazemzadeh & Aliaksei Laureshyn & Lena Winslott Hiselius & Enrico Ronchi, 2020. "Expanding the Scope of the Bicycle Level-of-Service Concept: A Review of the Literature," Sustainability, MDPI, vol. 12(7), pages 1-30, April.
    18. Ou, Hui & Tang, Tie-Qiao & Rui, Ying-Xu & Zhou, Jie-Ming, 2018. "Modeling electric bicycle’s abnormal behavior at a signalized intersection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 511(C), pages 218-231.
    19. Zhiwei Chen & Yucong Hu & Jutint Li & Xing Wu, 2020. "Optimal Deployment of Electric Bicycle Sharing Stations: Model Formulation and Solution Technique," Networks and Spatial Economics, Springer, vol. 20(1), pages 99-136, March.
    20. Xingchen Yan & Tao Wang & Xiaofei Ye & Jun Chen & Zhen Yang & Hua Bai, 2018. "Recommended Widths for Separated Bicycle Lanes Considering Abreast Riding and Overtaking," Sustainability, MDPI, vol. 10(9), pages 1-16, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:12:y:2020:i:3:p:799-:d:311752. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.