Accurate　prediction　of　the　traffic　state　can　help　to　solve　the　problem　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,　which　is　based　on　Multi-kernel　Support　Vector　Machine　(MSVM)　and　Adaptive　Particle　Swarm　Optimization　(APSO).　Firstly,　we　explore　both　the　nonlinear　and　randomness　characteristic　of　traffic　flow,　and　hybridize　Gaussian　kernel　and　polynomial　kernel　to　constitute　the　MSVM.　Secondly,　we　optimize　the　parameters　of　MSVM　with　a　novel　APSO　algorithm　by　considering　both　the　historical　and　real-time　traffic　data.　We　evaluate　our　algorithm　by　doing　thorough　experiment　on　a　large　real　dataset.　The　results　show　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　prediction　results　are　more　accurate　compared　to　four　baseline　methods.　©　2017　IEEE.