The　safety　performance　function　(SPF)　is　an　extensively　employed　tool　in　road　safety　assessment.　However,　traditional　modeling　methods　often　fall　short　of　effectively　capturing　the　intricate　interdependencies　among　diverse　traffic　variables.　To　address　this　limitation,　a　feature　importance　analyzable　resilient　deep　neural　network　(RDNN)　is　proposed　as　an　alternative　approach.　This　model　begins　with　an　explainable　autoencoder　that　delineates　the　relationship　between　observed　collisions　and　road　characteristics.　Subsequently,　it　introduces　a　priori　unsupervised　feature　importance　analysis　process　that　enriches　the　original　input　data.　The　enhanced　input　is　then　processed　by　a　novel　RDNN,　featuring　an　automated　Gaussian　transfer　function　and　resilient　supervised　learning,　both　meticulously　designed　for　precise　modeling.　Ultimately,　the　efficacy　of　the　proposed　framework　is　demonstrated　through　several　case　studies　on　real-world　applications,　utilizing　data　collected　from　highways　in　Canada　and　the　U.S.