Use ADAS Data to Predict Near-Miss Events: A Group-Based Zero-Inflated Poisson Approach

• URL: http://arxiv.org/abs/2509.02614v1
• Date: Sun, 31 Aug 2025 04:13:32 GMT
• Title: Use ADAS Data to Predict Near-Miss Events: A Group-Based Zero-Inflated Poisson Approach
• Authors: Xinbo Zhang, Montserrat Guillen, Lishuai Li, Xin Li, Youhua Frank Chen,
• Abstract summary: We analyze driving behavior big data to understand how people drive and powers applications such as risk evaluation, insurance pricing, and targeted intervention.<n>We show that the traditional statistical models underfit the dataset.<n>We propose a set of zero-inflated Poisson frameworks that learn latent behavior groups and fit offset-based count models via EM to yield calibrated, interpretable weekly risk predictions.
• Score: 7.242131400289439
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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