Advanced Transport Engineering Techniques for Monitoring Driving Habits Through in-Vechile Telematics Systems

Abstract

Shahin Mirbakhsh and Mahdi Azizi

Global road traffic fatalities are on the rise, largely due to preventable driving behaviors. In-vehicle telematics has emerged as a technology capable of enhancing driving behavior, widely adopted by insurance companies to monitor their clients' behaviors. This systematic review synthesizes how invehicle telematics has been modeled and analyzed. The review conducted electronic searches on Scopus and Web of Science, selecting studies with a sample size of at least 10 participants, data collected over multiple days, and publication dates from 2010 onwards. Forty-five relevant papers were included, with 27 rated as "good" in quality assessment. The literature showed a split in focus regarding in-vehicle telematics. Some studies explored its utility for insurance purposes, while others investigated its impact on driving behavior. Machine learning analyses were prevalent, particularly in studies focusing on insurance outcomes. Acceleration, braking, and speed were the most frequently analyzed variables. Future research should include demographic information to understand how in-vehicle telematics influences driving behaviors across different demographic groups. Additionally, employing multi-level models would better capture the hierarchical nature of telematics data, which includes individual trip data for each driver

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