Advancing usage-based insurance - a contextual driving risk modelling and analysis approach

Most researchers and insurers evaluate driver's risks solely based on user global positioning system (GPS) trajectories. A new approach is proposed that collects individualised driving behaviour data from smartphone GPS module, combined with geographical network information and dynamic traffic conditions, to identify driving risk factors and evaluate driving behaviours in various contexts. The multi-source data reveals real world activity patterns as to when, where and how an individual driver performs, from which performance measurements on both the trip and driver level are defined to measure the risks. In addition, the relationship between the defined driving performance measurements and accident rate can be examined and verified by combining the crash history of the drivers. Numeric analysis on the trip level demonstrates that driving behaviour is context-sensitive, and the principal component analysis performed at the driver level shows that pedal operation and driving speed are the two most important performance measurements to characterise an individual's driving pattern. The subsequent correlation analysis of crash history and driving performances verify both pedal operation and driving speed are significantly related to more at-fault accidents, which validates the modelling and analysis efforts. The findings of their study further existing knowledge and provide foundations for advanced pay-as-you-drive-and-you-save insurance pricing.