In the assisted driving-based Intelligent Transportation System (ADITS) featuring speed-guided functionality, intelligent vehicles are crucial for enhancing transportation sustainability. However, the impact of ADITS on transportation sustainability at different penetration rates remains unclear. This study combines road testing with simulation to address this gap. By integrating road test data, traffic survey data, ADITS algorithms, and Monte Carlo uncertainty analysis, the study validates the simulation results’ reliability. Correction coefficients are provided to refine energy consumption and carbon emissions simulations. The findings indicate that the differences between simulation data and on-road test data are minimal. However, under varying penetration rate environments, the differences in most variables are statistically significant. At 100% penetration, the adjusted energy use in congested traffic stands at 1.05 MJ/km, with carbon emissions of 81.6 g/km. In uncongested scenarios, these values drop to 0.65 MJ/km and 55.4 g/km, respectively. With the increasing penetration rate, energy efficiency and decarbonization efficiency gradually improve, achieving an optimization of 23%−27%. Intelligent vehicles optimized for uncongested conditions exhibit smoother driving patterns, mitigating aggressive maneuvers and contributing to green transport. In congested scenarios, intelligent vehicles face constraints from leading vehicles, limiting speed optimization. At lower penetration rates (0.25), ADITS can worsen traffic conditions. However, with higher penetration rates, speed fluctuations decrease, leading to more uniform road speeds, reduced high-speed accelerations, and lower energy consumption and carbon emissions. These findings provide theoretical support for the implementation and development of intelligent transportation systems.