With　the　development　of　location-based　services　and　smart　terminals,　skyline　query　technique　has　been　used　widely　in　intelligent　transportation　systems.　In　skyline　queries,　the　areas　and　keywords　queried　by　users　have　a　great　impact　on　the　quality　of　the　query　results　and　users　may　only　be　interested　in　the　closer　results　of　the　skyline　query.　However,　current　approaches　for　the　continuous　skyline　query　limit　the　area　of　the　skyline　query　to　a　specific　area　in　the　road　network,　which　leads　to　that　many　useful　query　results　cannot　be　retrieved.　To　this　end,　an　innovative　continuous　skyline　query　approach　in　city　range　is　proposed　in　this　paper,　where　a　multi-scale　area　divisions　of　the　urban　road　network　are　provided　to　find　the　optimize　query　scale　and　area.　In　our　approach,　first,　the　dominant　area　of　each　intersection　node　in　the　road　network　is　established　based　on　the　Voronoi.　Then,　all　Points　of　Interest　(POIs)　are　divided　into　the　dominant　area　of　each　intersection　node.　After　that,　the　intersection　node　aggregation　algorithm　(INAA),　link　remolding　algorithm　(LMA)　and　link　fitting　algorithm　(LFA)　are　proposed　to　reduce　the　number　of　intersection　nodes　in　the　road　network,　so　as　to　increase　the　dominant　area　of　the　remaining　intersection　nodes　and　the　number　of　POIs　in　these　nodes.　Finally,　a　better　query　scale　by　considering　the　efficiency　and　quality　of　the　query　is　given　through　the　studies.