Rainy-weather speed limit strategies on highways with variable longitudinal slopes

Abstract

Rainfall substantially degrades highway traffic safety by reducing pavement friction, shortening driver perception distance, and amplifying the adverse effects of longitudinal grade on vehicle dynamics. To support refined speed management under rainy-weather conditions, this study develops a dynamic safe speed-limit model that jointly accounts for pavement friction, longitudinal slope, and perception distance. An improved Intelligent Driver Model (IDM) is further proposed to represent rainy-weather car-following behavior and speed adaptation. The proposed model was evaluated using the Hangzhou West-Fuxi highway section, and simulation experiments were conducted under different rainfall intensities and slope conditions. Model validation showed that the simulated speeds were consistent with observed speed characteristics, with RMSE values of 2.13–3.08 km/h and MAPE values of 2.31%−4.12%. The safety evaluation results indicated that the proposed speed-limit strategy reduced conflict rates by 7.1%, 73.6%, and 82.4% across the three rainfall scenarios compared with the unrestricted-speed condition. Sensitivity analysis further showed that increasing rainfall intensity reduced the pavement friction coefficient, increased braking distance, and narrowed the safety boundary, particularly on downhill sections. The results indicate that no additional speed restriction is generally required for rainfall intensities of 0–1.0 mm/min, whereas rainfall intensities of 1.0–5.0 mm/min require progressively stricter speed limits. A longitudinal slope of +1% provides the most favorable operating condition, while a −3% slope represents the most adverse case. These findings provide a quantitative basis for adaptive highway speed-limit control under rainy-weather conditions.