Accurate　traffic　estimation　on　urban　networks　is　a　prerequisite　for　efficient　traffic　detection,　congestion　warning,　and　transportation　schedule.　The　current　estimation　methods　can　be　roughly　divided　into　model-driven　and　the　data-driven　methods.　The　estimation　accuracy　of　the　model-driven　methods　cannot　satisfy　certain　applications.　Meanwhile,　the　data-driven　methods　have　the　disadvantages　of　poor　generalization　ability　and　weak　interpretability.　To　overcome　these　challenges,　this　paper　proposes　a　framework　named　the　physics-informed　spatiotemporal　graph　convolution　neural　network　(PSTGCN)　based　on　physics-informed　deep　learning　theories.　The　PSTGCN　uses　a　spatiotemporal　graph　convolution　neural　network　combined　with　traffic　flow　models　to　estimate　the　traffic　state.　The　proposed　model　not　only　considers　the　temporal　and　spatial　dependence　of　traffic　flow　but　also　abides　by　the　internal　law　of　traffic　flow.　Furthermore,　the　estimation　objects　of　the　proposed　model　are　multiple　variables　that　comprehensively　represent　the　traffic　state.　Experiments　on　real-world　traffic　data　reveal　the　error　of　the　PSTGCN　is　reduced　by　38.39%　compared　to　the　baselines.　Also,　the　PSTGCN　can　achieve　a　similar　prediction　effect　as　the　baselines　by　using　half　of　the　global　spatial　information.　These　results　demonstrate　that　the　PSTGCN　outperforms　the　state-of-the-art　models　in　urban　traffic　estimation　and　is　robust　under　variable　road　conditions　and　data　scales.