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.

中文翻译：

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农村道路设施环境智能优化方法

本研究开发了一种从驾驶员视觉感知到设施环境（即道路设施和周围景观）的智能优化方法，以调整乡村道路的运行速度。与以往严重依赖专家经验且耗时的方法不同，该方法可以快速生成设施环境的优化视觉图像，并及时验证优化效果。本研究建立了视觉道路图式模型，从驾驶员的视觉感知中量化设施环境，并利用自解释理论提出了考虑原始设施环境特征的自动优化方案确定方法。然后，使用循环一致的生成对抗网络自动生成优化的设施环境图像。为了验证优化效果，使用随机森林预测优化设施环境的运行速度。案例研究表明，该方法可以有效优化原有运行速度超限速20%以上的设施环境，整个过程只需1小时，远少于以往的几个月或几年。总体而言，本研究提高了优化设施环境的智能化水平，增强了农村道路安全。