A self-adjusted macroscopic traffic model with fog correction factor was proposed in this paper by studying the freeway characteristics with the effect of variable speed limit (VSL) control under the foggy weather condition. The freeway different visibility and curve radius were the input variables of Takagi-Sugeno (T-S) model, the fog correction factor was the output variables of T-S model. The fog correction factor can be regulated in real time by using the T-S model according the freeway different visibility and curve radius. The proposed traffic model with the effect of variable speed limit control under foggy weather condition was regulated in real time by the correction factor. The grey wolf optimization (GWO) algorithm was used to optimize parameters of traffic model and T-S model. The mean absolute percentage error of speed and density were used to evaluate proposed traffic model. The freeway traffic data under the foggy weather condition were derived from VISSIM and the experiment simulations are applied in MATLAB. Compared with conventional traffic model, the proposed traffic model improves speed and density performance by 41. 5% and 10.5%, respectively. Simulation results show that proposed traffic model is more accurate to reflect freeway characteristics with effect of VSL control under foggy weather condition.