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Development of Methodology for Defining a Pattern of Drivers Mobile Phone Usage While Driving

Author

Listed:
  • Petar Čolić

    (Ericsson Nikola Tesla, Krapinska 45, 10000 Zagreb, Croatia)

  • Marijan Jakovljević

    (Faculty of Transport and Traffic Sciences, University of Zagreb, Vukelićeva 4, 10000 Zagreb, Croatia)

  • Krešimir Vidović

    (Ericsson Nikola Tesla, Krapinska 45, 10000 Zagreb, Croatia)

  • Marko Šoštarić

    (Faculty of Transport and Traffic Sciences, University of Zagreb, Vukelićeva 4, 10000 Zagreb, Croatia)

Abstract

Mobile phone usage during driving causes driver distraction, which negatively affects road traffic safety and is becoming an increasingly common cause of traffic accidents. This paper proposes a new methodology, based on anonymized datasets from telecommunication networks and analytical possibilities of data science, which has as its goal to identify a pattern of usage of the mobile phone (in particular voice calls and applications) by drivers while driving. Based on this finding, the methodology is used to identify segments of the road infrastructure on which mobile phone usage is more intense. Applying this methodology enables stakeholders in the road traffic safety system to propose targeted measures (educational, legal, or engineering) precisely on the sections where it is most vital to do so in order to reduce mobile phone usage and enhance road safety. The proposed methodology was implemented and demonstrated on two pilot sections on state roads in the Republic of Croatia. Research results point to the fact that the percentage of drivers who use the mobile phone for voice calls or for app usage is exceptionally high (10.68% to 33.90%) and presents a serious threat to road traffic safety.

Suggested Citation

  • Petar Čolić & Marijan Jakovljević & Krešimir Vidović & Marko Šoštarić, 2022. "Development of Methodology for Defining a Pattern of Drivers Mobile Phone Usage While Driving," Sustainability, MDPI, vol. 14(3), pages 1-28, February.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:3:p:1681-:d:740058
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    References listed on IDEAS

    as
    1. Wencai Sun & Yihao Si & Mengzhu Guo & Shiwu Li, 2021. "Driver Distraction Recognition Using Wearable IMU Sensor Data," Sustainability, MDPI, vol. 13(3), pages 1-17, January.
    2. Sónia Soares & Carlos Campos & João Miguel Leitão & António Lobo & António Couto & Sara Ferreira, 2021. "Distractive Tasks and the Influence of Driver Attributes," Sustainability, MDPI, vol. 13(9), pages 1-20, May.
    Full references (including those not matched with items on IDEAS)

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