In　order　to　timely　and　accurately　estimate　traffic　densities　of　the　whole　road　network　and　to　identify　congestion　in　an　urban　freeway　network　when　traffic　sensors　do　not　cover　the　freeway　network　completely　and　only　local　measurement　data　can　be　utilized.　A　proportional-integral　(PI)　observer　approach　was　proposed　based　on　a　macroscopic　traffic　flow　model.　Firstly,　the　urban　freeway　network　was　modeled　by　embedding　the　cell　transmission　model　(CTM)　into　the　dynamic　graph　hybrid　automata　(DGHA),　and　then　a　piecewise　affine　linear　system　(PWALS)　model　was　deduced.　Moreover,　the　PI　observer　was　designed　on　the　basis　of　this　model,　and　the　proportional　gain　and　integral　gain　were　computed　via　using　the　Linear　Matrix　Inequality　(LMI)　method.　Especially,　the　traffic　congestion　was　identified　by　checking　whether　the　estimated　densities　were　greater　than　the　critical　ones.　Finally,　as　examples,　the　Jingtong　freeway　and　East　Third　Ring　freeway　were　given　to　demonstrate　the　feasibility　of　the　proposed　approach.　©　2018,　Editorial　Department　of　Journal　of　Beijing　University　of　Technology.　All　right　reserved.