From Massive Trajectory Data to Traffic Modeling for Better Behavior Prediction in a Usage-Based Insurance Context

Abstract

Usage-Based Insurance (UBI) is an insurance framework that has made its appearance in the last few years. It allows direct measurement of the traveling of policyholders, hence the growing interest of the industry to better understand driving behaviors. UBI generates large data volumes, from which events can be extracted, like harsh brakes or accelerations. Still, these events are measured without contextual information, which limits their explanatory power. Traffic is one of these types of contextual information that may have great potential, but access to such data remains an issue. Solutions exist, like traffic data from external providers, but for insurance companies that conduct business over large areas, this could result in very large costs. This paper demonstrates that data from insurance companies acquired via UBI can be used to model traffic. A method based on link travel time is proposed and tested on four Canadian cities. Then, routes created with the model are compared with those created using Google Maps. The results show that for 38 routes with an average length of around 5 km, the difference between the travel time of the routes of the proposed model and Google Maps is as small as one second on average.