Advancing Usage-Based Insurance -- A Contextual Driving Risk Modelling and Analysis Approach
Abstract
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.
Recommended Citation
X. Hu et al., "Advancing Usage-Based Insurance -- A Contextual Driving Risk Modelling and Analysis Approach," IET Intelligent Transport Systems, vol. 13, no. 3, pp. 453 - 460, Institution of Engineering and Technology (IEE), Mar 2019.
The definitive version is available at https://doi.org/10.1049/iet-its.2018.5194
Department(s)
Civil, Architectural and Environmental Engineering
Keywords and Phrases
Accidents; Automobile drivers; Behavioral research; Digital storage; Global positioning system; Motor transportation; Principal component analysis, Context sensitive; Correlation analysis; Driving performance; Dynamic traffic conditions; Geographical networks; Modelling and analysis; Performance measurements; Real-world activities, Risk assessment
International Standard Serial Number (ISSN)
1751-956X; 1751-9578
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2019 Institution of Engineering and Technology (IEE), All rights reserved.
Publication Date
01 Mar 2019
Comments
This research was supported by the Federal Highway Administration (FHWA)"Pay-As-You-Drive-And-You-Save (PAYDAYS) Insurance Actuarial Study"™ project. Contract award number DTFH61-13-C-00033.