ASRM Seminar -- Sheldon Lin -- Assessing Driving Risk using Telematics Data: a Wavelet Transform Approach

Title: Assessing Driving Risk using Telematics Data: a Wavelet Transform Approach

Abstract: Vehicle telematics provides granular data for dynamic driving risk assessment, but most existing methods to assess driving risk rely on aggregated metrics/summary statistics. As a result, they do not fully exploit the rich time-series structure of trip data. In this project, we propose the use of wavelet transform to capture key driving patterns of trips which in turn are used to evaluate driver and trip-level behaviors. By incorporating measures of both frequency and severity of acceleration events, this approach enables to distinguish aggressive and inattentive driving from normal driving behavior, as demonstrated in a control study. Furthermore, the approach enhances interpretability, reduces misclassification, and provides a flexible foundation for telematics-based risk assessment. This is joint work with Andrei Badescu and Jongtaek Lee.

Bio of the speaker: X. Sheldon Lin, an Associate of the Society of Actuaries and the Canadian Institute of Actuaries, is currently a Professor of Actuarial Science at the University of Toronto. He is an Editor of Insurance: Mathematics and Economics, a top research journal in actuarial science and a past Co-Editor of the North American Actuarial Journal. He also served on the Editorial Board of Management Science (Finance Section).  He has published extensively in insurance risk modeling and management. His recent research focuses on data-driven modeling for insurance ratemaking and effective simulation for large insurance portfolios. The research aims to develop new and implementable methodology and technologies for insurance industry.