Unconventional　events　exacerbate　the　imbalance　between　regional　transportation　demand　and　limited　road　network　resources.　Scientific　and　efficient　path　planning　serves　as　the　foundation　for　rapidly　restoring　equilibrium　to　the　road　network.　In　real　large-scale　road　networks,　especially　during　emergencies,　it　is　usually　difficult　to　obtain　or　predict　accurate　dynamic　traffic　network　flows　in　real-time,　which　is　used　to　support　equilibrium　path　planning.　Moreover,　the　traditional　iterative　methods　cannot　meet　the　real-time　demand　of　emergency　equilibrium　path　planning　decision　generation.　To　solve　the　above　problems,　this　paper　proposes　a　hybrid　control　architecture　for　path　planning　based　on　equilibrium　traffic　assignment　theory.　The　architecture　introduces　the　travelers＇　real-time　travel　data　and　constructs　a　spatio-temporal　neural　network,　which　captures　the　evolution　of　traffic　network　loads.　Adaptive　multi-graph　fusion　technology　is　used　to　mix　the　background　traffic　flow　data　and　the　traveler＇s　real-time　Origin-Destination　(OD)　data,　to　mine　the　dynamic　correlation　between　the　traffic　state　and　the　travelers＇　travel.　Based　on　the　real-time　prediction　of　dynamic　network　states,　equilibrium　mapping　learning　is　carried　out　to　pre-allocate　potential　travel　demands　and　construct　equilibrium　traffic　graphs　based　on　system　optimization　traffic　assignment.　Finally,　individual　evacuation　path　strategies　are　generated　online　in　a　data-driven　manner　in　real　time　to　achieve　improved　resilience　in　the　transportation　system.