Analysis and optimisation of the impact of fog on highway traffic flow and safety performance

This study devised a fog recognition model and simulated traffic flow in low visibility. It initially built a cloud image recognition model based on convolutional neural network and support vector machine. Subsequently, a mixed traffic flow model was developed for low-visibility conditions. The results showed that the Gaussian kernel function achieved the highest fog image recognition accuracy, reaching 92.58%, while the polynomial kernel function had the lowest accuracy of 84.19%. When five experiments were conducted, the fog image recognition model in this study exhibited the highest accuracy (0.94), recall (0.875), and F1 score (F1) (0.9). In a vertical driving formation, vehicles ahead travelled faster, indicating that the convergence speed and stability of the full speed difference within the formation were improved. The enhanced intelligent driver model demonstrated minimal speed fluctuations, with all vehicles in an 8-car fleet reaching a stable driving speed within 40 s. This implies excellent stability of the improved intelligent driver model. In conclusion, the model developed in this research shows promising practical applications in fog recognition and traffic flow management under low visibility, and has positive significance for improving highway safety performance.