Short-term　traffic　forecasting　is　important　for　the　development　of　an　intelligent　traffic　management　system.　Critical　to　the　performance　of　the　traffic　prediction　model　utilized　in　such　a　system　is　accurate　representation　of　the　spatiotemporal　traffic　characteristics.　This　can　be　achieved　by　integrating　spatiotemporal　traffic　information　or　the　dynamic　traffic　characteristics　in　the　modeling　process.　The　currently　employed　spatiotemporal　k-nearest　neighbor　(STKNN)　model　is　based　on　the　spatial　heterogeneity　and　adaptive　spatiotemporal　parameters　of　the　traffic　to　improve　the　prediction　accuracy.　However,　the　non-stationary　characteristics　of　the　traffic　cannot　be　fully　represented　by　simply　modeling　the　entire　time　range　or　all　the　time　partitions　based　on　experience.　We　therefore　developed　a　dynamic　STKNN　model　(D-STKNN)　for　short-term　traffic　forecasting　based　on　the　non-stationary　spatiotemporal　pattern　of　the　road　traffic.　The　different　traffic　patterns　along　the　road　are　first　automatically　determined　using　an　affinity　propagation　clustering　algorithm.　The　Warped　K-Means　algorithm　is　then　used　to　automatically　partition　the　time　periods　for　each　traffic　pattern.　Finally,　the　D-STKNN　model　is　developed　based　on　the　three-dimensional　spatiotemporal　tensor　data　models　for　the　different　road　segments　with　different　traffic　patterns　during　different　time　periods.　The　D-STKNN　model　was　verified　through　extensive　experiments　performed　using　actual　vehicular　speed　datasets　collected　from　city　roads　in　Beijing,　China,　and　expressways　in　California,　U.S.A.　The　proposed　model　outperforms　existing　seven　baselines　in　different　time　periods　under　different　traffic　patterns.　The　results　confirmed　the　imperative　of　considering　the　non-stationary　spatiotemporal　traffic　pattern　in　developing　a　model　for　short-term　traffic　prediction.