Kavli Affiliate: Li Xin Li
| First 5 Authors: Xinbo Zhang, Xinbo Zhang, , ,
| Summary:
Driving behavior big data leverages multi-sensor telematics to understand how
people drive and powers applications such as risk evaluation, insurance
pricing, and targeted intervention. Usage-based insurance (UBI) built on these
data has become mainstream. Telematics-captured near-miss events (NMEs) provide
a timely alternative to claim-based risk, but weekly NMEs are sparse, highly
zero-inflated, and behaviorally heterogeneous even after exposure
normalization. Analyzing multi-sensor telematics and ADAS warnings, we show
that the traditional statistical models underfit the dataset. We address these
challenges by proposing a set of zero-inflated Poisson (ZIP) frameworks that
learn latent behavior groups and fit offset-based count models via EM to yield
calibrated, interpretable weekly risk predictions. Using a naturalistic dataset
from a fleet of 354 commercial drivers over a year, during which the drivers
completed 287,511 trips and logged 8,142,896 km in total, our results show
consistent improvements over baselines and prior telematics models, with lower
AIC/BIC values in-sample and better calibration out-of-sample. We also
conducted sensitivity analyses on the EM-based grouping for the number of
clusters, finding that the gains were robust and interpretable. Practically,
this supports context-aware ratemaking on a weekly basis and fairer premiums by
recognizing heterogeneous driving styles.
| Search Query: ArXiv Query: search_query=au:”Li Xin Li”&id_list=&start=0&max_results=3