Refining the classification of combined alignment sections on mountainous freeways and analyzing the spatio-temporal...

Combined alignment sections of mountainous freeways often feature complex geometric configurations-such as downhill sag/convex curves, slope-changing curves, and uphill curves-that significantly affect crash risk. Existing studies typically apply homogeneous segmentation and broad classifications (e.g., downhill, uphill, sag/convex), which fail to capture the specific effects of geometric combinations on crash frequency. In addition, traffic operations and weather conditions in mountainous areas exhibit strong seasonal variation, and using annual data may obscure important patterns, making quarterly analysis necessary. The interaction of complex geometry, dynamic traffic flow, and adverse winter weather results in nonlinear spatio-temporal effects that conventional models cannot effectively capture. To address this, the study integrates road geometry, traffic operation, and environmental data into a Zero-Inflated Negative Binomial (ZINB) model enhanced with Gaussian processes, systematically analyzing the nonlinear spatio-temporal effects on crash frequency. Results show that the proposed model outperforms spatial- or temporal-only models in prediction accuracy (RMSE = 0.566) and model fit (LOOIC = 5961.2), with the variance of spatio-temporal interaction effects estimated at 1.35 (95 % BCI: 1.12-1.58), indicating substantial nonlinear influence. Key findings include a 56 % increase in crash frequency on straight downhill sag curves, a 2 % reduction on straight uphill convex curves, an 80.3 % increase for every additional 1,000 vehicles in daily traffic flow, and a 28.8 % decrease in crash frequency for each 1 °C rise in temperature. The study presents a refined classification and modeling framework that significantly improves crash risk identification and prediction for mountainous freeways, offering strong support for traffic safety management.