Short-term　traffic　forecasting　plays　an　important　part　in　intelligent　transportation　systems.　Spatiotemporal　k-nearest　neighbor　models　(ST-KNNs)　have　been　widely　adopted　for　short-term　traffic　forecasting　in　which　spatiotemporal　matrices　are　constructed　to　describe　traffic　conditions.　The　performance　of　the　models　is　closely　related　to　the　spatial　dependencies,　the　temporal　dependencies,　and　the　interaction　of　spatiotemporal　dependencies.　However,　these　models　use　distance　functions　and　correlation　coefficients　to　identify　spatial　neighbors　and　measure　the　temporal　interaction　by　only　considering　the　temporal　closeness　of　traffic,　which　result　in　existing　ST-KNNs　that　cannot　fully　reflect　the　essential　features　of　road　traffic.　This　study　proposes　an　improved　spatiotemporal　k-nearest　neighbor　model　for　short-term　traffic　forecasting　by　utilizing　a　multi-view　learning　algorithm　named　MVL-STKNN　that　fully　considers　the　spatiotemporal　dependencies　of　traffic　data.　First,　the　spatial　neighbors　for　each　road　segment　are　automatically　determined　using　cross-correlation　under　different　temporal　dependencies.　Three　spatiotemporal　views　are　built　on　the　constructed　spatiotemporal　closeness,　periodic,　and　trend　matrices　to　represent　spatially　heterogeneous　traffic　states.　Second,　a　spatiotemporal　weighting　matrix　is　introduced　into　the　ST-KNN　model　to　recognize　similar　traffic　patterns　in　the　three　spatiotemporal　views.　Finally,　the　results　of　traffic　pattern　recognition　under　these　three　spatiotemporal　views　are　aggregated　by　using　a　neural　network　algorithm　to　describe　the　interaction　of　spatiotemporal　dependencies.　Extensive　experiments　were　conducted　using　real　vehicular-speed　datasets　collected　on　city　roads　and　expressways.　In　comparison　with　baseline　methods,　the　results　show　that　the　MVL-STKNN　model　greatly　improves　short-term　traffic　forecasting　by　lowering　the　mean　absolute　percentage　error　between　28.24%　and　46.86%　for　the　city　road　dataset　and,　between　53.80%　and　90.29%,　for　the　expressway　dataset.　The　results　suggest　that　multi-view　learning　merits　further　attention　for　traffic-related　data　mining　under　such　a　dynamic　and　data-intensive　environment,　which　owes　to　its　comprehensive　consideration　of　spatial　correlation　and　heterogeneity　as　well　as　temporal　fluctuation　and　regularity　in　road　traffic.