Global positioning systems (GPS) have revolutionized transportation by providing real-time location and navigation information. Although GPS technology has significantly enhanced mobility and efficiency, its potential role in traffic crashes remains understudied, partly due to a lack of information in the crash data that associates GPSs with crashes. However, growing concerns over distracted driving and navigation errors caused by GPS systems have gained attention—studies estimate that over 200,000 crashes annually involve GPS-related distractions in the US alone. Additionally, high-profile incidents, such as drivers following incorrect GPS directions onto hazardous routes, have raised safety concerns, indicating potential risks associated with GPS reliance. In recent years, the availability of crash narratives has added more information to crash databases, including GPS-related details. This study addresses this gap by examining the factors associated with GPS-related crashes using crash data from Ohio collected between 2017 and 2022. Crash narratives were used to identify GPS-related crashes; then a matching case–control was used to obtain the non-GPS-related crashes. Furthermore, the study utilized a logistic regression model to identify patterns, contributing factors, and their influences on GPS involvement in crashes. Results indicate that GPS-related crashes are disproportionately concentrated at intersections, interchanges, and complex road geometries, particularly under adverse weather conditions. Driver age and weather conditions are significantly associated with GPS crashes—younger drivers and commercial vehicles are involved in more crashes. These findings highlight the need for targeted interventions, such as improved GPS technology, disruptive technologies such as augmented reality, infrastructure enhancements, and driver education, to mitigate the risks associated with GPS use while driving and improve overall road safety.


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    Titel :

    Examining Patterns of GPS-Related Traffic Crashes: Insights from a Matched Case–Control Approach through Crash Narratives


    Beteiligte:
    Kutela, Boniphace (Autor:in) / Chengula, Tumlumbe Juliana (Autor:in) / Ngeni, Frank (Autor:in) / Lippu, Clement (Autor:in) / Kidando, Emmanuel (Autor:in) / Liu, Jinli (Autor:in) / Das, Subasish (Autor:in)


    Erscheinungsdatum :

    01.09.2025




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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