With　the　vigorous　development　of　transportation　infrastructure　in　various　countries,　the　traffic　network　within　the　city　is　becoming　more　and　more　complex,　and　when　an　emergency　occurs　in　one　or　more　areas　of　the　city,　it　will　inevitably　cause　traffic　congestion　in　the　area　and　keep　spreading.　There　are　still　many　challenges　to　solve　the　urban　emergency　route　planning　problem.　In　this　paper,　we　have　employed　a　double　layer　search　structure,　where　we　have　empowered　the　traditional　A*　model　with　a　neural　network,　to　construct　a　region-level　dynamic　path　planning　model　known　as　“Double　Layer　A*”.　The　model　divides　the　road　network　into　two　layers,　and　implements　the　outer　layer　and　inner　layer　search.　In　the　outer　layer　search,　we　use　the　historical　cab　travel　data　for　training　to　achieve　the　general　direction　planning;　in　the　inner　layer　search,　we　update　the　original　planning　according　to　the　changes　of　the　road　condition　characteristics　of　the　regional　nodes,　and　perform　the　re-planning　in　real　time.　We　conducted　experimental　evaluations　using　the　road　network　data　of　Beijing,　and　the　results　showed　that　compared　to　a　single-layer　search　structure　path　planning　model,　our　Double　layer　A*　model　planned　paths　with　higher　similarity　in　land　characteristics,　connectivity,　and　average　connectivity　between　adjacent　nodes,　which　demonstrates　the　effectiveness　and　reasonableness　of　the　Double　layer　A*　model　in　emergency　path　planning.　IEEE