Redefining ramp influence area for curved diverging and merging freeway segments using crash data

Freeway Ramp Influence Areas (RIAs) are commonly defined using fixed buffer distances despite evidence that driver behavior, roadway geometry, and traffic operations produce spatially heterogeneous crash patterns near ramps. This study proposes a data-driven framework to empirically identify where ramp-related crash influence stabilizes rather than assuming a predetermined distance using crash data from the Washington State from 2018 to 2023.

This study developed an analytical framework to delineate ramp influence areas. First, Negative Binomial Generalized Additive Models (NBGAMs) were used to quantify how geometric design, traffic demand, and operational features influence crash frequency across curved and straight entry and exit ramps. Second, Negative Binomial gradient boosting (XGBoost) with grouped site-level cross-validation was used to generate out-of-fold predicted crash-risk curves, which were then analyzed using a piecewise change-point estimator with site-level spatial bootstrap resampling to identify the crash influence distance (τ) and its uncertainty for each ramp configuration.

Results reveal a geometry-dependent hierarchy of influence distances. Curved entry ramps exhibited the longest downstream disturbance (τ ≈ 1,800 ft), substantially exceeding conventional thresholds. Straight entry ramps showed localized and demand-sensitive influence (τ ≈ 300-1,000 ft). Curved exit ramps produced compact influence zones (τ ≈ 500-900 ft), while straight exit ramps were highly localized (τ ≈ 300 ft). These findings demonstrate a directional asymmetry in crash propagation: merging disturbances diffuse downstream whereas diverging disturbances dissipate rapidly near the gore.

This research offers a data-driven framework for refining RIA definitions based on geometric alignment. By identifying geometry-specific thresholds, transportation agencies can develop more accurate safety assessments and improve freeway ramp design standards. The study's findings support the adoption of flexible, risk-based RIA delineation approaches to enhance crash mitigation efforts in complex ramp environments.