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Application of Logistic Regression Model to Assess the Impact of Smartwatch on Improving Road Traffic Safety: A Driving Simulator Study

Author

Listed:
  • Duško Pešić

    (Road Traffic Safety Agency, 11000 Belgrade, Serbia)

  • Dalibor Pešić

    (Faculty of Transport and Traffic Engineering, University of Belgrade, 11000 Belgrade, Serbia)

  • Aleksandar Trifunović

    (Faculty of Transport and Traffic Engineering, University of Belgrade, 11000 Belgrade, Serbia)

  • Svetlana Čičević

    (Faculty of Transport and Traffic Engineering, University of Belgrade, 11000 Belgrade, Serbia)

Abstract

Speeding is one of the most relevant risk behaviours for serious and fatal road traffic accidents, particularly among young drivers, being the cause of approximately every third road traffic accident. Due to this background, many road traffic safety campaigns are aimed at reducing speeding among young drivers. However, the effects of campaigns aimed at complying with speed limits for young drivers have significantly fewer effects than other campaigns. For these reasons, an experimental study was conducted to examine how young drivers react to the speeding campaign, which was shown to them on a smartwatch while driving in a driving simulator. Speeding results were compared for three scenarios: no campaign, a billboard campaign and a smartwatch campaign. The experiment involved 102 participants with an average age of 21 years. The results showed that participants were six times more likely to comply with the speed limit if a campaign was shown on a smartwatch than when shown on billboards.

Suggested Citation

  • Duško Pešić & Dalibor Pešić & Aleksandar Trifunović & Svetlana Čičević, 2022. "Application of Logistic Regression Model to Assess the Impact of Smartwatch on Improving Road Traffic Safety: A Driving Simulator Study," Mathematics, MDPI, vol. 10(9), pages 1-12, April.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:9:p:1403-:d:799726
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    Cited by:

    1. Lili Zheng & Yanlin Zhang & Tongqiang Ding & Fanyun Meng & Yanlin Li & Shiyu Cao, 2022. "Classification of Driver Distraction Risk Levels: Based on Driver’s Gaze and Secondary Driving Tasks," Mathematics, MDPI, vol. 10(24), pages 1-23, December.

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