Paper Info
Title
MoCha: Large-Scale Driving Pattern Characterization for Usage-based Insurance
Abstract
ABSTRACTGiven widely adopted vehicle tracking technologies, usage-based insurance has been a rising market over the past few years. With potential discounts from insurance companies, customers voluntarily install sensing devices in their vehicles for insurance companies, which are utilized to analyze their historical driving patterns to derive the risks of future driving. However, it is challenging to characterize and predict driving patterns, especially for new users with limited data. To address this issue, we propose and evaluate a system called MoCha to accurately characterize driving patterns for usage-based insurance. The key question we aim to explore with MoCha is whether we can fully explore long-term driving patterns of new users with only limited historical data of themselves by leveraging abundant data of other users and contextual information. To answer this question, we design (i) a multi-level driving pattern modeling component to capture the spatial-temporal dependency on both individual and group level, and (ii) a multi-task learning method to utilize underlying relations of driving metrics and predict multiple driving metrics simultaneously. We implement and evaluate MoCha with real-world on-board diagnostics data from a large insurance company with more than 340,000 vehicles. Further, we validate the usefulness of MoCha by predicting driving risks based on real-world claim data in a Chinese city, Shenzhen.
Year
DOI
Venue
2021
10.1145/3447548.3467114
Knowledge Discovery and Data Mining
Keywords
DocType
Citations 
Usage-based Insurance, Driving Patterns, User Mobility
Conference
0
PageRank 
References 
Authors
0.34
0
8
Name
Order
Citations
PageRank
Zhihan Fang1517.77
Guang Yang2195.90
Dian Zhang3628.27
Xiaoyang Xie4152.83
Guang Wang5287.58
Yang Yu615138.02
Fan Zhang7384.95
Desheng Zhang835645.96