The　spatiotemporal　learning　has　been　one　of　the　most　burning　research　topics　in　traffic　data　analytics　for　traffic　forecasting,　which　is　instrumental　in　developing　the　intelligent　transportation　systems.　Capturing　accurately　and　efficiently　complex　spatiotemporal　dependencies　of　the　road　network　is　a　critical　prerequisite　of　the　traffic　spatiotemporal　forecasting.　This　study　proposes　a　multi-view　spatiotemporal　learning　(MVSTL)　framework,　which　combines　the　fast　parallel　learning　(FPL)　and　the　serial　learning　(SL)　to　juggle　the　receptive　field　with　the　fitting　capability　of　the　model.　The　FPL　is　proposed　to　capture　the　spatiotemporal　correlations　synchronously　and　efficiently　by　reducing　the　number　of　parameters　significantly.　As　a　supplement　to　the　FPL,　the　SL,　which　extracts　the　spatial　and　temporal　features　serially,　can　expand　the　spatial/temporal　receptive　fields　and　avoid　information　overwriting.　The　evaluation　of　the　MVSTL　is　carried　out　by　employing　four　sets　of　traffic　data　from　the　LA　freeway　system,　and　the　experiment　results　reveal　that　the　MVSTL　is　able　to　perform　better　than　the　advanced　methods　in　prediction　accuracy.