With　the　developing　of　smart　sensors　and　mobile　devices　produces　an　increasing　volume　of　data,　and　it　captures　the　states　of　transportation　infrastructures.　Such　data　are　collected　and　uploaded　frequently,　which　forms　the　heavy　data　calculation　and　storage.　Moreover,　state　of　monitored　object　may　be　keeping　the　same　or　slight　change　according　to　a　certain　state　during　a　period,　such　as　moving　vehicles　on　a　certain　path　with　a　basic　uniform　speed.　Therefore,　if　trajectory　pattern　of　vehicles　can　be　obtained　through　the　state　change　mode,　scale　and　update　frequency　of　the　data　can　be　greatly　reduced.　Based　on　the　above-mentioned　ideas,　we　are　aware　that　the　trajectory　data　storage　is　divided　into　traceability　storage　and　vector　storage,　where　original　sampled　data　from　sensing　device,　and　state　vectors　are　extracted　from　the　analysis　of　the　original　sample　data.　In　this　way,　only　a　relatively　small　amount　of　vector　data　is　stored.　The　system　will　not　only　effectively　reduce　the　frequency　of　sampling　data　storage,　but　also　reduce　query　and　analysis　operations　involved　with　the　amount　of　data.　The　vector　function　is　used　to　represent　road　network　with　indexes,　and　the　data　query　based　on　the　road　network　is　used　to　extract　the　semantic　information.　Our　experimental　results　show　that　our　proposed　methodologies　have　significant　improvements　in　intelligent　transportation.