This study introduces a data-driven framework that integrates kinetic energy principles into proactive intersection safety management. Building on the Federal Highway Administration’s (FHWA) Safe System for Intersection (SSI) model, this research proposes the Kinetic Velocity Index (KVI), a model calibrated using categorized crash data to better represent the physical dynamics of intersection crashes. Unlike the SSI model, the KVI estimates the probability of fatal and serious injuries (FSIs) at the crash level, simplifying application while preserving alignment with safe system principles. The study used over 900,000 two-vehicle intersection crashes from the years 2013 to 2024. The model was developed using data from Georgia and validated with an external dataset from Massachusetts. The KVI maintained strong predictive performance (R 2 = 0.991 in Georgia; R 2 = 0.922 in Massachusetts), demonstrating its generalizability. Importantly, the KVI enables proactive safety screening by estimating severity risk even at locations with no prior FSIs, offering a more reliable and risk-oriented alternative to models that rely solely on past crash outcomes, which can be subject to random variation and may not reflect underlying crash risk. It also supports integration with historical data in weighted risk models. Because the KVI is easy to compute, interpretable, and built on physics-based principles, it provides transportation professionals with a more precise and scalable tool for identifying high-risk locations and evaluating the safety implications of design alternatives. Furthermore, the KVI framework is easily adaptable and can be locally recalibrated to reflect regional crash patterns, making it applicable across diverse roadway contexts.
Cabe et al. (Mon,) studied this question.