Traffic　patterns　in　the　spatiotemporal　network　are　affected　by　temporal　dynamics　and　spatial　correlations.　The　network　flows　have　different　strengths　interacting　at　various　implicit　layers,　and　this　dynamic　process　needs　to　be　further　explored.　Predicting　future　traffic　based　on　historical　data　from　transportation　IoT　has　been　well　studied,　however,　most　of　the　works　focus　on　traffic　dynamics　in　the　homogeneous　spatial　or　temporal　structure.　When　the　spatiotemporal　graph　structure　turns　complex,　it　becomes　a　challenge　to　capture　the　deep　traffic　patterns　on　it.　In　this　paper,　a　heterogeneous　modular　flows　graph　is　constructed　to　characterize　the　implied　spatiotemporal　correlations　within　the　traffic　data.　Then,　we　proposed　a　Multilayer　Graph　Skip　Temporal　Convolution　Network　(MGSTCN)　which　extracts　skip　aggregated　representations　of　node　status　to　the　modular　flows　graph.　And　an　extended　random　walk　on　diverse　modular　graphs　is　used　to　learn　the　spatial　dependencies.　The　experiments　based　on　real　traffic　networks　confirmed　that　the　MGSTCN　has　a　better　performance　compared　to　the　spatiotemporal　homogeneous　methods.