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Personal cabinetDynamic driver risk scoring based on telematics data in rental fleets
Bibliographic description of the article for the citation:
Melnyk Ihor. Dynamic driver risk scoring based on telematics data in rental fleets//Science online: International Scientific e-zine - 2026. - №5. - https://nauka-online.com/en/publications/technical-sciences/2026/5/02-53/
Annotation: The article is devoted to the search for algorithms that allow drivers and fleet operators to reduce road risks based on telematics data, behavioral analysis and risk scoring. The relevance of the topic is due to the fact that in the conditions of digitalization of the short-term car rental market, driving safety can no longer be ensured only by traditional control of violations or technical monitoring of the vehicle. Dynamic assessment of driver behavior is becoming more important, which allows identifying risk patterns directly during the trip. The purpose of the article is to substantiate the features of dynamic assessment of driving risk based on telematics data in short-term car rental fleets on the US market. The study used general scientific methods of analysis, generalization, comparison and systematization of scientific literature. The author's methodology for comprehensive risk assessment is also presented, which can be adapted to the conditions of the US market and integrated into modern telematics, insurance and fleet-management systems. The article also shows that approaches to assessing driving risk have different construction logics. Some models are focused on the driver's perception of danger, while others use sensory signals, a smartphone, GPS or OBD-II data to recognize driving style. This allowed us to show that it is advisable to assess the risk in rental fleets not as a single event, but as a sequence of driver behavioral reactions within a specific trip. It is this approach that creates the basis for earlier detection of unstable or aggressive driving behavior. The practical significance of the study is that the proposed methodology can be used by international fleets, short-term car rental companies, insurance companies that use usage-based insurance models, as well as state and municipal transport systems. Its strengths include the ability to prevent fleet accidents through early detection of risky behavior, reduce insurance and repair costs, create an AI-model of behavioral risk scoring of the driver, scale to various telematics and fleet-management platforms, and transform fleets into data-driven safety and risk management systems.