Traffic safety evaluation has traditionally relied on police-reported crash statistics, often considered the “gold standard” because they directly correlate with fatalities, injuries, and property damage. However, relying on historical crash data for predictive modeling presents significant challenges, because such data is inherently a “lagging” indicator. Also, crashes are statistically rare events on arterial and local roads, so it can take years to accumulate sufficient data to establish a valid safety profile for a specific road segment. This sparsity paired with inconsistent reporting standards across regions complicates the development of robust risk prediction models. Proactive safety assessment requires “leading” measures: proxies for crash risk that correlate with safety outcomes but occur more frequently than crashes.
In “From Lagging to Leading: Validating Hard Braking Events as High-Density Indicators of Segment Crash Risk”, we evaluate the efficacy of hard-braking events (HBEs) as a scalable surrogate for crash risk. An HBE is an instance where a vehicle’s forward deceleration exceeds a specific threshold (-3m/s²), which we interpret as an evasive maneuver. HBEs facilitate network-wide analysis because they are sourced from connected vehicle data, unlike proximity-based surrogates like time-to-collision that frequently necessitate the use of fixed sensors. We established a statistically significant positive correlation between the rates of crashes (of any severity level) and HBE frequency by combining public crash data from Virginia and California with anonymized, aggregated HBE information from the Android Auto platform.
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