To　achieve　accurate　traffic　forecasting,　previous　research　has　employed　inner　and　outer　aggregation　for　information　aggregation,　and　attention　mechanisms　for　heterogeneous　spatiotemporal　dependency　learning,　which　results　in　inefficient　model　learning.　While　learning　efficiency　is　critical　due　to　the　need　for　updating　frequently　the　model　to　alleviate　the　impact　of　concept　drift,　limited　work　has　focused　on　improving　it.　For　efficient　learning　and　accurate　forecasting,　this　study　proposes　the　dynamic　spatiotemporal　straight-flow　network　(DSTSFN).　Breaking　the　aggregation　paradigms　employing　both　inner　and　outer　aggregation,　which　may　be　redundant,　the　DSTSFN　designs　a　straight-flow　network　that　employs　bipartite　graphs　to　learn　directly　the　dependencies　between　the　source　and　target　nodes　for　outer　aggregation　only.　Instead　of　the　attention　mechanisms,　the　dynamic　graphs/networks,　which　outdo　static　ones　by　possessing　time-varying　dependencies,　are　designed　in　the　DSTSFN　to　distinguish　the　dependency　heterogeneity,　making　the　model　relatively　streamlined.　Additionally,　two　learning　strategies　based　on　respectively　the　curriculum　and　transfer　learning　are　developed　to　further　improve　the　learning　efficiency　of　the　DSTSFN.　Our　study　could　be　the　first　work　designing　the　learning　strategies　for　the　multi-step　traffic　predictor　based　on　dynamic　spatiotemporal　graphs.　The　learning　efficiency　and　forecasting　accuracy　are　demonstrated　by　experiments,　which　show　that　the　DSTSFN　can　outperform　not　only　the　state-of-the-art　(SOTA)　predictor　for　accuracy　by　achieving　a　2.27%　improvement　in　accuracy　and　requiring　only　8.98%　of　the　average　training　time,　but　also　the　SOTA　predictor　for　efficiency　by　achieving　a　9.26%　improvement　in　accuracy　and　requiring　91.68%　of　the　average　training　time.