Although drivers can adequately adjust their operating speed according to the road curva-ture, they show a lack of recognition regarding the pavement friction conditions. In this regard, inappropriate speed selection on Horizontal Curves (HCs) with reduced surface fric-tion can lead to a remarkable rate of run-off-road, sideswipe, head-on, and rollover crashes, especially on rural highways. Aligned with the Connected Vehicle (CV) Pilot Program on Interstate-80 in Wyoming, this study scrutinizes how CV advisory/warning messages can enhance traffic safety on slippery HCs. To this aim, a roadway consists of two HCs with reg-ular and slippery pavement conditions was designed in a high-fidelity driving simulator experiment. A total of 24 professional truck drivers were recruited to drive the simulated roadway under CV and non-CV environments. In the CV scenario, drivers were informed about the pavement conditions and the advisory speeds before entering HCs. In contrast, no messages were given to non-CV drivers. Truck drivers' behaviors in both scenarios were quantified using four Kinematic-based Surrogate Measures of Safety (K-SMoS), including deviation from the pathway, instantaneous acceleration, lateral speed, and steering angle. CVs' trajectories were statistically compared to non-CVs in terms of the central tendency and dispersion using the Wilcoxon Signed-Rank Test (WSRT) and Median Absolute Deviation (MAD), respectively. The results of WSRT depicted, under the effect of CV advi-sory/warning messages and throughout the slippery HC, the central tendency of four K-SMoS could be shifted toward zero by 23% up to 99%. This shifting is associated with a sig-nificant safety enhancement that potentially can reduce the likelihood of curve-related crashes on slippery HCs. It was revealed that the variation in drivers' behavior on the slip-pery HC could be minimized in the CV environment, where 54% up to 95% reduction in the dispersions of four K-SMoS were observed, leading to more certainty in drivers' behavior. (c) 2021 Elsevier Ltd. All rights reserved.