The　graph-based　traffic　forecasting　is　generally　realized　on　the　assumption　of　sufficient　data,　which　could　be　impractical　in　the　regions　without　well-deployed　mobile　sensors　or　data-processing　facilities.　Recent　studies　have　developed　a　solution　with　the　cross-region　transfer　learning,　i.e.　transferring　traffic　knowledge　from　the　source　regions　to　target　ones,　whose　traffic　data　and　computing　resources　are　limited.　Nevertheless,　relevant　issues,　including　initialization　selection　and　domain　adaptation,　have　not　been　effectively　tackled　in　the　cross-region　graph-based　traffic　forecasting.　This　paper　proposes　the　cluster-granularity　spatiotemporal　transfer　(CGSTT),　which　transfers　the　cluster-granularity　knowledge　from　the　source　region　to　target　one　for　the　cross-region　graph-based　traffic　forecasting,　as　not　all　source　knowledge　is　positive　to　the　target　region.　Additionally,　the　domain　adaptation　is　achieved　by　the　dual　alignment　consisting　of　the　covariate　alignment　and　label　alignment　of　the　source/target　data,　making　the　proposed　CGSTT　adapt　to　the　target　region　efficiently.　The　superiority　of　the　proposed　method　over　ten　compared　baseline　methods　for　both　short-term　and　long-term　predictions　is　demonstrated　by　the　conducted　experiments　on　four　tasks,　which　show　that　it　outperforms　the　state-of-the-art　method　by　achieving　an　8.89%　average　improvement　in　forecasting　accuracy.　The　PyTorch　implementation　of　the　CGSTT　is　available　at　https://github.com/canyangguo/CGSTT.