IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0229653.html
   My bibliography  Save this article

Investigating influence factors of traffic violations at signalized intersections using data gathered from traffic enforcement camera

Author

Listed:
  • Chuanyun Fu
  • Hua Liu

Abstract

To effectively reduce traffic violations that often cause severe crashes at signalized intersections, exploring their contributing factors seems hugely urgent and essential. This study attempted to investigate the influence factors of wrong-way driving (WWD), red-light-running (RLR), violating traffic markings (VTM), and driving in the inaccurate oriented lane (DIOL) at signalized intersections by using data collected from traffic enforcement camera in Hohhot, China. To this end, an ordinary multinomial logit model was developed. By considering the unobserved heterogeneity between observations, a random effects multinomial logit model was proposed as well. After that, the marginal effects of explanatory variables were computed. The outcomes showed that non-local vehicles were more likely to commit WWD and VTM than local vehicles. WWD and RLR frequently occurred in the daytime and evening (6:00–23:59), and on most days within a week. RLR and DIOL mainly happened in June and July. The left-turn lane ratio significantly increased RLR and DIOL. The cloudy, partly cloudy, and rainy days obviously increased WWD and VTM. The temperature from 21 to 30 degrees centigrade was apparently associated with the higher likelihoods of RLR and DIOL. According to the findings of this study, some intervention measures, targeting different vehicle types and considering temporal factors, road, and weather conditions, were recommended to reduce WWD, RLR, VTM, and DIOL at signalized intersections.

Suggested Citation

  • Chuanyun Fu & Hua Liu, 2020. "Investigating influence factors of traffic violations at signalized intersections using data gathered from traffic enforcement camera," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-20, March.
    • Handle: RePEc:plo:pone00:0229653
    DOI: 10.1371/journal.pone.0229653
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0229653
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0229653&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0229653?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. 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)

    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. Zhanguo Song & Yanyong Guo & Yao Wu & Jing Ma, 2019. "Short-term traffic speed prediction under different data collection time intervals using a SARIMA-SDGM hybrid prediction model," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-19, June.
    2. 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.
    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. 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.
    5. 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.
    6. Zhenggan Cai & Fulu Wei & Zhenyu Wang & Yongqing Guo & Long Chen & Xin Li, 2021. "Modeling of Low Visibility-Related Rural Single-Vehicle Crashes Considering Unobserved Heterogeneity and Spatial Correlation," Sustainability, MDPI, vol. 13(13), pages 1-24, July.

    More about this item

    Statistics

    Access and download statistics

    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:plo:pone00:0229653. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.