Accurate　traffic　flow　forecasting　at　urban　intersections　is　critical　for　optimizing　transportation　infrastructure　and　reducing　congestion.　This　manuscript　introduces　a　novel　framework,　the　Physics-Guided　Spatio-Temporal　Graph　Neural　Network　(PG-STGNN),　specifically　designed　for　traffic　flow　prediction.　By　integrating　the　principles　of　traffic　flow　physics　with　advanced　spatio-temporal　graph　neural　network　algorithms,　the　framework　captures　complex　spatio-temporal　dependencies　in　traffic　networks.　PG-STGNN　adopts　a　stepwise　approach,　addressing　key　performance　metrics　like　queue　formation　and　signal　timing　complexities　at　intersections.　To　validate　its　effectiveness,　the　model　was　applied　to　real-world　traffic　data　from　the　Yizhuang　District　of　Beijing.　Compared　to　traditional　models　such　as　ARIMA,　KNN,　and　Random　Forest,　PG-STGNN　significantly　improves　prediction　accuracy,　achieving　MAPE　reductions　of　19.9　%,　18.6　%,　6.1　%,　20.7　%,　5.0　%,　1.8　%,　and　1.1　%　against　KNN,　ARIMA,　RF,　BP,　T-GCN,　STGCN,　and　ST-ED-RMGC,　respectively.　With　the　lowest　MAPE　(9.452　%),　MAE　(2.485),　and　RMSE　(4.364),　PG-STGNN　demonstrates　superior　prediction　performance.　These　results　underscore　its　potential　to　provide　reliable　short-term　traffic　forecasts,　offering　essential　insights　for　the　strategic　planning　and　management　of　urban　intelligent　transportation　systems.　©　2025　The　Author(s)