Driving Behavior Index and Early Detection of High-Risk Drivers Using Connected Vehicle Data

Abstract

This study presents a comprehensive approach to quantifying journey-level driving behavior using connected vehicle (CV) trajectory data. A count and severity-weighted driving behavior index was developed through confirmatory factor analysis, integrating acceleration, braking, speeding, and cruising metrics to assess the overall risk level of each trip. The index was applied to over 330,000 trips across Iowa, revealing a skewed distribution, indicating that while most trips involved moderate behavior, a notable subset exhibited aggressive patterns such as frequent speed limit violations and abrupt maneuvers. The study further explored whether behaviors in the first five minutes of a trip are associated with driving tendencies during the remainder of the journey. Using quantile regression, the analysis demonstrated that early trip cruising was consistently linked to safer driving, while early speeding and braking events were associated with elevated risk throughout the remainder of the journey. Acceleration variables showed relatively weaker associations with the remainder journey behavior. These associations varied across different trip durations, suggesting that trips of similar length should be compared when applying performance metrics or behavior-based scoring methods. These findings from this study support both reactive and proactive safety strategies by enabling real-time alerts during risky trips and post-trip identification of high-risk journeys for targeted feedback or intervention. As CV datasets continue to grow in coverage and quality, future work should explore collaborations with commercial operators and integrate high-frequency telemetry to enable proactive risk management.