IDEAS home Printed from https://ideas.repec.org/a/gam/jscscx/v11y2022i3p130-d772021.html
   My bibliography  Save this article

How Data Mining Can Improve Road Safety in Cities

Author

Listed:
  • Elena Butsenko

    (Department of Business Informatics, Institute of Digital Management Technologies and Information Security, Ural State University of Economics, 620144 Ekaterinburg, Russia)

Abstract

Traffic collisions pose a serious problem for cities due to the annually increasing number of vehicles. Information about incidents that occur on roads is important for the corresponding monitoring bodies, authorities, and emergency services. To ensure traffic safety, the data have to be visible, clean, and transparently displayed. This research was, therefore, aimed at developing a methodology for monitoring motor vehicle collision data and applying visualization techniques to evidence from New York City. The method showed that the largest number of motor vehicle traffic crashes occurred in Lower Manhattan due to its high population and traffic density. With these data, the road agencies of the city can put potentially dangerous road sections under control and make them safer for both drivers and pedestrians. Further development of the system may be associated with data analytics and visualization, resulting in new layers of heatmaps that not only provide details on car collision hotspots, which serve as the main target indicator for traffic safety authorities, but also break them down into social facilities, such as schools. This feature will enable assessment of how safe it is around a school and the evaluation of the impact of an underpass or a traffic enforcement camera on the number of collisions. The motor vehicle traffic crash (MVTC) monitoring system will help in comparing city districts and regions in terms of safety, seeing trends, realizing what exactly is happening at interchanges, and understanding the reasons behind. The methodology, in addition, can be supplemented with an analysis of risk factors for MVTCs, the efficiency of adopted measures and road renovations that are carried out, and many other functions.

Suggested Citation

  • Elena Butsenko, 2022. "How Data Mining Can Improve Road Safety in Cities," Social Sciences, MDPI, vol. 11(3), pages 1-11, March.
    • Handle: RePEc:gam:jscscx:v:11:y:2022:i:3:p:130-:d:772021
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2076-0760/11/3/130/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2076-0760/11/3/130/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Vitória Albuquerque & Ana Oliveira & Jorge Lourenço Barbosa & Rui Simão Rodrigues & Francisco Andrade & Miguel Sales Dias & João Carlos Ferreira, 2021. "Smart Cities: Data-Driven Solutions to Understand Disruptive Problems in Transportation—The Lisbon Case Study," Energies, MDPI, vol. 14(11), pages 1-25, May.
    2. Li, Kun & Xu, Haocheng & Liu, Xiao, 2022. "Analysis and visualization of accidents severity based on LightGBM-TPE," Chaos, Solitons & Fractals, Elsevier, vol. 157(C).
    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. Feng, Fenling & Zhang, Jiaqi & Liu, Chengguang, 2023. "Integrated pricing mechanism of China Railway Express whole-process logistics based on the Stackelberg game," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    2. Pérez-Sala, Luis & Curado, Manuel & Tortosa, Leandro & Vicent, Jose F., 2023. "Deep learning model of convolutional neural networks powered by a genetic algorithm for prevention of traffic accidents severity," Chaos, Solitons & Fractals, Elsevier, vol. 169(C).
    3. Mireille Megnidio-Tchoukouegno & Jacob Adedayo Adedeji, 2023. "Machine Learning for Road Traffic Accident Improvement and Environmental Resource Management in the Transportation Sector," Sustainability, MDPI, vol. 15(3), pages 1-19, January.
    4. Shahriari, Zahra & Nazarimehr, Fahimeh & Rajagopal, Karthikeyan & Jafari, Sajad & Perc, Matjaž & Svetec, Milan, 2022. "Cryptocurrency price analysis with ordinal partition networks," Applied Mathematics and Computation, Elsevier, vol. 430(C).

    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:jscscx:v:11:y:2022:i:3:p:130-:d:772021. 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.