The cross-section speed data of a continuous long downhill section of an expressway in Southwest China was collected, in order to study the operating characteristics of trucks on the long downhill section, improve the effectiveness of the operating speed prediction model, and ensure that vehicles can drive safely on the long downhill section. The temporal and spatial distribution characteristics of truck speed and the degree of dispersion of vehicle speed were analyzed, and the speed distribution characteristics of trucks on the long and large downhill road was tested through Q-Q probability diagram and single-sample K-S test, thus the speed distribution characteristics of trucks on the long downhill section were obtained. The error analysis of the operating speed model was carried out according to the distribution characteristics, so as to determine the cause of the error and correct the variable. Finally, a prediction and correction model of truck operating speed was established, and the effectiveness of the mo-del was compared and analyzed before and after the correction. The results show when the truck is traveling on a long downhill section, the speed gradually decreases first and then tends towards stability as the downhill distance increases; the degree of dispersion of vehicle speed increases with the increase of downhill distance and traffic vo-lume. Truck speed characteristics do not change significantly over time; truck speed in the long and large downhill sections conforms to the law of Logistic distribution, and the speed is highly concentrated and symmetrical to the peak speed value. The existing speed prediction model has deviations mainly because these models do not consider the influence of traffic density. The relative error of the truck running speed prediction correction model established based on this is reduced by 4% to 14%, which significantly improved the effectiveness. This can provide a theore-tical basis for the study of the long downhill running speed and improve the downhill safety of trucks.