Integrating Spatial and Temporal Effects in Seat-Belt Compliance Assessment with Telematics Data

TL;DR

This study leverages telematics data to develop advanced spatiotemporal models that accurately assess seat-belt compliance across counties, revealing significant geographic and temporal patterns to inform targeted safety interventions.

Contribution

It introduces a novel approach using high-resolution telematics data with beta-regression models incorporating spatial and temporal effects for seat-belt compliance analysis.

Findings

Models with both spatial and temporal effects outperform simpler models.

Vehicle miles traveled and income are key predictors of compliance.

Telematics data significantly improves model accuracy and inference.

Abstract

Seat belt use remains one of the most effective measures for reducing vehicle occupant fatalities and injuries. Yet, seat-belt compliance across different locales demands far more granular data than traditional, roadside surveys can provide. These surveys are spatially sparse, temporally intermittent, and costly to administer, often providing coarse-grained snapshots insufficient for capturing dynamic behavioral patterns or localized disparities. Telematics data emerges as a transformative alternative, offering continuous, high-resolution driver event records, such as seat belt latch status, across vast geographic areas. This granular and scalable data enables the application of advanced spatiotemporal models that more accurately reflect the complex interactions driving seatbelt use. This study utilizes telematics data to generate county-level seat belt compliance metrics for Iowa in 2022, employing a suite of beta-regression models that incorporate spatial and temporal random effects. The study findings demonstrate that models including both spatial and temporal components outperform those with spatial or temporal effects alone, underscoring the importance of jointly accounting for geographic clustering and temporal dynamics. Among explanatory variables, vehicle miles traveled (VMT) and per capita income emerge as significant predictors of compliance rates. The significant spatial and temporal effects highlight that telematics-based granular data substantially enhances model fit and inference quality. The results demonstrate that integrating granular telematics data within sophisticated spatiotemporal frameworks significantly improves inference, providing policymakers with precise insights for targeted interventions and advancing traffic safety research.