Surrogate safety measures derived from in-vehicle telematics data

Road safety monitoring is essential for achieving safer mobility. Traditionally studied with statistical methods, the field is evolving due to AI and the growing availability of in-vehicle telematics data.

A Paper titled Learning-based methods for spatial road safety analysis using in-vehicle telematics data: A systematic review authored by Simone Paradiso, Apostolos Ziakopoulos and George Yannis has been published in Journal of Safety Research.

This Paper presents a structured review of the existing literature at the intersection of learning-based methods, spatial analysis, and surrogate safety measures derived from in-vehicle telematics data, following PRISMA guidelines, where 44 studies were identified and analyzed. The studies were analyzed and narratively synthesized focusing on data collection methods, feature engineering processes, and their implications on the selection of the spatial scale, with methods ranging from traditional econometrics to cutting-edge deep learning techniques.

The findings suggest that advancements in AI and telematics data are reshaping road safety research, providing new tools to interpret safety analyses and generate actionable insights, while clarifying the relationships among data sources, feature selection, and spatial scale would strengthen the analytical framework and improve understanding for safer mobility.