Highlights In-vehicle telematics has been used to monitor driving behaviour and insurer risk. Machine learning is the dominant method for analysis of in-vehicle telematics data. Speeding, braking, and distance variables are the most useful telematics variables. Currently the effects of telematics feedback on driving behaviour are unknown. Future research should consider individual trip differences and driver differences.

    Abstract Background Road traffic deaths are increasing globally, and preventable driving behaviours are a significant cause of these deaths. In-vehicle telematics has been seen as technology that can improve driving behaviour. The technology has been adopted by many insurance companies to track the behaviours of their consumers. This systematic review presents a summary of the ways that in-vehicle telematics has been modelled and analysed. Methodology Electronic searches were conducted on Scopus and Web of Science. Studies were only included if they had a sample size of 10 or more participants, collected their data over at least multiple days, and were published during or after 2010. 45 relevant papers were included in the review. 27 of these articles received a rating of “good” in the quality assessment. Results We found a divide in the literature regarding the use of in-vehicle telematics. Some articles were interested in the utility of in-vehicle telematics for insurance purposes, while others were interested in determining the influence that in-vehicle telematics has on driving behaviour. Machine learning analyses were the most common forms of analysis seen throughout the review, being especially common in articles with insurance-based outcomes. Acceleration, braking, and speed were the most common variables identified in the review. Conclusion We recommend that future studies provide the demographical information of their sample so that the influence of in-vehicle telematics on the driving behaviours of different groups can be understood. It is also recommended that future studies use multi-level models to account for the hierarchical structure of the telematics data. This hierarchical structure refers to the individual trips for each driver.


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    Titel :

    A systematic review of the use of in-vehicle telematics in monitoring driving behaviours


    Beteiligte:
    Boylan, James (Autor:in) / Meyer, Denny (Autor:in) / Chen, Won Sun (Autor:in)


    Erscheinungsdatum :

    2024-02-21




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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