Traffic　sign　recognition　is　crucial　for　sustainable　utilization　of　road　assets.　However,　on　real　roads,　traffic　signs　are　often　obscured,　making　their　recognition　challenging.　Unfortunately,　existing　research　lacks　specific　analysis　of　the　impact　of　traffic　sign　occlusion　and　methods　to　improve　recognition　in　such　scenarios.　This　study　quantitatively　investigates　the　relationship　between　the　degree　of　traffic　sign　occlusion　and　recognition　accuracy.　Moreover,　a　dedicated　deep　learning　model　is　proposed,　utilizing　a　multi-scale　convolutional　stacked　input　layer,　to　enhance　the　recognition　of　obscured　traffic　signs.　Using　the　Chinese　Traffic　Sign　Recognition　Database　(TSRD),　this　study　analyzes　the　recognition　performance　of　four　traffic　sign　categories:　indication　signs,　prohibition　signs,　speed　limit　signs,　and　warning　signs,　under　different　occlusion　levels.　Three　widely　used　deep　learning　methods,　Visual　Geometry　Group　(VGG),　Residual　Network　(ResNet),　and　Dense　Convolutional　Network　(DenseNet),　are　compared　with　the　dedicated　model.　Experimental　results　demonstrate　a　significant　decrease　in　recognition　accuracy　when　traffic　signs　are　obscured.　Importantly,　the　proposed　dedicated　model　outperforms　the　other　methods,　achieving　accuracies　of　80.95%,　90.50%,　and　97.22%　in　the　scenarios　of　50%　occlusion,　25%　occlusion,　and　no　occlusion,　respectively.　This　study　holds　implications　for　sustainable　utilization　of　road　assets.