IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i7p4231-d785875.html
   My bibliography  Save this article

Analyzing Safety Concerns of (e-) Bikes and Cycling Behaviors at Intersections in Urban Area

Author

Listed:
  • Jian Wang

    (China Design Group Co., Ltd., Nanjing 210004, China)

  • Ye Chen

    (School of Transportation, Southeast University, Nanjing 210096, China)

  • Dawei Chen

    (School of Transportation, Southeast University, Nanjing 210096, China)

Abstract

Extensive effort has been devoted to examining the causal relationship between contributing factors and injury severities. Given the important role of riders’ behaviors in traffic conflicts, this paper aims to analyze the causal effects of traffic conflicts resulting from riders’ behaviors at intersections. The authors collected video data on 152 traffic conflicts caused by riders’ dangerous behaviors in Jiangning District, China. This paper proposes a Bayesian-structural equation modeling (BSEM) approach. Based on the obtained BSEM path coefficient diagram, the factor loadings and path coefficients are analyzed to unveil the potential influence of factors, including personal features, dangerous behavior tendency, temporal and spatial characteristics of dangerous behavior, and the external environment. The results show that compared to human factors, environmental factors have a less direct impact on the severity of traffic conflicts; instead, they have an indirect positive impact on traffic conflicts by affecting behaviors. That is, if riders judge that road conditions are not suitable to conduct dangerous behaviors, they become more cautious in view of current road conditions and time revenue. Furthermore, dangerous cycling behaviors that continue to encroach on the time and space of motorized vehicles are prone to be more dangerous. The dangerous behaviors that continuously encroach on the time and space of motor vehicles (e.g., disobeying traffic signals and riding in a motorway) are significant predictors of serious conflicts. Considering the heterogeneity of riding behavior, these findings could be applied to develop effective education and intervention programs for preventing riders’ high-risk behaviors and improving the traffic environment.

Suggested Citation

  • Jian Wang & Ye Chen & Dawei Chen, 2022. "Analyzing Safety Concerns of (e-) Bikes and Cycling Behaviors at Intersections in Urban Area," Sustainability, MDPI, vol. 14(7), pages 1-17, April.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:7:p:4231-:d:785875
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/7/4231/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/14/7/4231/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Boukis, Achilleas & Punjaisri, Khanyapuss & Balmer, John M.T. & Kaminakis, Kostas & Papastathopoulos, Avraam, 2021. "Unveiling front-line employees’ brand construal types during corporate brand promise delivery: A multi-study analysis," Journal of Business Research, Elsevier, vol. 131(C), pages 673-685.
    2. Richard Scheines & Herbert Hoijtink & Anne Boomsma, 1999. "Bayesian estimation and testing of structural equation models," Psychometrika, Springer;The Psychometric Society, vol. 64(1), pages 37-52, March.
    Full references (including those not matched with items on IDEAS)

    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. Leonidas A. Zampetakis & Melina Vekini & Vassilis Moustakis, 2009. "Entrepreneurial orientation, access to financial resources, and product performance in the Greek commercial TV industry," The Service Industries Journal, Taylor & Francis Journals, vol. 31(6), pages 897-910, April.
    2. Igor Filatotchev & Natalia Isachenkova & Tomasz Mickiewicz, 2007. "Corporate Governance, Managers' Independence, Exporting, and Performance of Firms in Transition Economies," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 43(5), pages 62-77, October.
    3. Ali Noudoostbeni & Kiran Kaur & Hashem Salarzadeh Jenatabadi, 2018. "A Comparison of Structural Equation Modeling Approaches with DeLone & McLean’s Model: A Case Study of Radio-Frequency Identification User Satisfaction in Malaysian University Libraries," Sustainability, MDPI, vol. 10(7), pages 1-16, July.
    4. Kees Montfort & Johan Oud, 2015. "Book Review," Psychometrika, Springer;The Psychometric Society, vol. 80(1), pages 257-258, March.
    5. David Kaplan & Chansoon Lee, 2018. "Optimizing Prediction Using Bayesian Model Averaging: Examples Using Large-Scale Educational Assessments," Evaluation Review, , vol. 42(4), pages 423-457, August.
    6. Lai-Fa Hung & Wen-Chung Wang, 2012. "The Generalized Multilevel Facets Model for Longitudinal Data," Journal of Educational and Behavioral Statistics, , vol. 37(2), pages 231-255, April.
    7. Díez-Mesa, Francisco & de Oña, Rocio & de Oña, Juan, 2018. "Bayesian networks and structural equation modelling to develop service quality models: Metro of Seville case study," Transportation Research Part A: Policy and Practice, Elsevier, vol. 118(C), pages 1-13.
    8. Asim Ansari & Kamel Jedidi & Sharan Jagpal, 2000. "A Hierarchical Bayesian Methodology for Treating Heterogeneity in Structural Equation Models," Marketing Science, INFORMS, vol. 19(4), pages 328-347, August.
    9. Galina Biedenbach & Thomas Biedenbach & Peter Hultén & Veronika Tarnovskaya, 2022. "Organizational resilience and internal branding: investigating the effects triggered by self-service technology," Journal of Brand Management, Palgrave Macmillan, vol. 29(4), pages 420-433, July.
    10. Dag Yngve Dahle, 2024. "Trust and Shout: The Reputation/Voice Tension in Schools and Hospitals," Corporate Reputation Review, Palgrave Macmillan, vol. 27(1), pages 52-69, February.
    11. Yuanyuan Gu & Richard Norman & Rosalie Viney, 2014. "Estimating Health State Utility Values From Discrete Choice Experiments—A Qaly Space Model Approach," Health Economics, John Wiley & Sons, Ltd., vol. 23(9), pages 1098-1114, September.
    12. Sik-Yum Lee & Ye-Mao Xia, 2008. "A Robust Bayesian Approach for Structural Equation Models with Missing Data," Psychometrika, Springer;The Psychometric Society, vol. 73(3), pages 343-364, September.
    13. Che Wan Jasimah Bt Wan Mohamed Radzi & Hashem Salarzadeh Jenatabadi & Maisarah Binti Hasbullah, 2015. "Firm Sustainability Performance Index Modeling," Sustainability, MDPI, vol. 7(12), pages 1-17, December.
    14. Hong-Tu Zhu & Sik-Yum Lee, 2001. "A Bayesian analysis of finite mixtures in the LISREL model," Psychometrika, Springer;The Psychometric Society, vol. 66(1), pages 133-152, March.
    15. Dingjing Shi & Xin Tong, 2017. "The Impact of Prior Information on Bayesian Latent Basis Growth Model Estimation," SAGE Open, , vol. 7(3), pages 21582440177, August.
    16. Lee, Sik-Yum & Song, Xin-Yuan, 2008. "On Bayesian estimation and model comparison of an integrated structural equation model," Computational Statistics & Data Analysis, Elsevier, vol. 52(10), pages 4814-4827, June.
    17. Fienberg, Stephen E. & Glymour, Clark & Scheines, Richard, 2003. "Expert statistical testimony and epidemiological evidence: the toxic effects of lead exposure on children," Journal of Econometrics, Elsevier, vol. 113(1), pages 33-48, March.
    18. Mahalingam Vasantha & Malaisamy Muniyandi & Chinnaiyan Ponnuraja & Ramalingam Srinivasan & Perumal Venkatesan, 2021. "Bayesian structural equation modeling for post treatment health related quality of life among tuberculosis patients," PLOS ONE, Public Library of Science, vol. 16(5), pages 1-10, May.
    19. Brian Chi-ang Lin & Siqi Zheng & Eleftherios Giovanis & Oznur Ozdamar, 2016. "Structural Equation Modelling And The Causal Effect Of Permanent Income On Life Satisfaction: The Case Of Air Pollution Valuation In Switzerland," Journal of Economic Surveys, Wiley Blackwell, vol. 30(3), pages 430-459, July.
    20. Piotr Tarka, 2018. "An overview of structural equation modeling: its beginnings, historical development, usefulness and controversies in the social sciences," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(1), pages 313-354, January.

    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:14:y:2022:i:7:p:4231-:d:785875. 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.