An intelligent optimization method for the facility environment on rural roads

Artikel i vetenskaplig tidskrift, 2024

This study develops an intelligent optimization method of the facility environment (i.e., road facilities and surrounding landscapes) from drivers’ visual perception to adjust operation speeds on rural roads. Different from previous methods that heavily rely on expert experience and are time-consuming, this method can rapidly generate optimized visual images of the facility environment and promptly verify the optimization effects. In this study, a visual road schema model is established to quantify the facility environment from drivers’ visual perception, and an automated optimization scheme determination approach considering the original facility environment characteristics is proposed using self-explaining theory. Then, Cycle-consistent generative adversarial network is used to automatically generate optimized facility environment images. To verify the optimization effect, operation speeds of the optimized facility environments are predicted using random forest. The case study shows that this method can effectively optimize the facility environment where original operation speeds are more than 20% over the speed limits, and the whole process only takes 1 h far less than several months or years in previous ways. Overall, this study advances the intelligence level in optimizing the facility environment and enhances rural road safety.