Accurate　estimation　of　the　traffic　state　can　help　to　address　the　issue　of　urban　traffic　congestion,　providing　guiding　advices　for　people＇s　travel　and　traffic　regulation.　In　this　paper,　we　propose　a　novel　short-term　traffic　flow　prediction　algorithm　based　on　an　adaptive　multi-kernel　support　vector　machine　(AMSVM)　with　spatial-temporal　correlation,　which　is　named　as　AMSVM-STC.　First,　we　explore　both　the　nonlinearity　and　randomness　of　the　traffic　flow,　and　hybridize　Gaussian　kernel　and　polynomial　kernel　to　constitute　the　AMSVM.　Second,　we　optimize　the　parameters　of　AMSVM　with　the　adaptive　particle　swarm　optimization　algorithm,　and　propose　a　novel　method　to　make　the　hybrid　kernel＇s　weight　adjust　adaptively　according　to　the　change　tendency　of　real-time　traffic　flow.　Third,　we　incorporate　the　spatial-temporal　correlation　information　with　AMSVM　to　predict　the　short-term　traffic　flow.　We　evaluate　our　algorithm　by　doing　thorough　experiment　on　real　data　sets.　The　results　demonstrate　that　our　algorithm　can　do　a　timely　and　adaptive　prediction　even　in　the　rush　hour　when　the　traffic　conditions　change　rapidly.　At　the　same　time,　the　proposed　AMSVM-STC　outperforms　the　existing　methods.