Spatiotemporal　prediction　modeling　of　traffic　is　an　important　issue　in　the　field　of　spatiotemporal　data　mining.　However,　it　is　encountering　multiple　challenges　such　as　the　global　spatiotemporal　correlation　between　predictive　tasks,　balanced　between　spatiotemporal　heterogeneity　and　the　global　predictive　power　of　the　model,　and　parameter　optimization　of　prediction　models.　Most　existing　short-term　traffic　prediction　methods　only　emphasize　spatiotemporal　dependence　and　heterogeneity,　so　it　is　difficult　to　get　satisfactory　prediction　accuracy.　In　this　paper,　spatiotemporal　multi-task　and　multi-view　feature　learning　models　based　on　particle　swarm　optimization　are　combined　to　concurrently　address　these　challenges.　First,　cross-correlation　is　used　to　construct　the　spatiotemporal　proximity　view,　periodic　view　and　trend　view　of　each　road　segment　to　characterize　spatiotemporal　dependence　and　heterogeneity.　Second,　the　prediction　results　of　three　spatiotemporal　views　are　obtained　using　a　set　of　kernels,　which　is　further　regarded　as　a　high-level　heterogeneous　semantic　feature　as　the　input　of　the　multi-task　multi-view　feature　learning　model.　Third,　additional　regularization　terms　(e.g.,　group　Lasso　penalty,　graph　Laplacian　regularization)　are　utilized　to　constrain　all　tasks　to　select　a　set　of　shared　features　and　ensure　the　relatedness　between　tasks　and　consistency　between　views,　so　that　the　predictive　model　has　a　good　global　predictive　ability　and　can　capture　global　spatiotemporal　correlation　in　the　road　network.　Finally,　particle　swarm　optimization　is　introduced　to　obtain　the　optimal　parameter　set　and　enhance　the　training　speed　of　the　proposed　model.　Experimental　studies　on　real　vehicular-speed　datasets　collected　on　city　roads　demonstrate　that　the　proposed　model　significantly　outperform　the　existing　nine　baseline　methods　in　terms　of　prediction　accuracy.　The　results　suggest　that　the　proposed　model　merits　further　attention　for　other　spatiotemporal　prediction　tasks,　such　as　water　quality,　crowd　flow,　owing　to　the　versatility　of　the　modeling　process　for　spatiotemporal　data.　©　2019　Elsevier　B.V.