Intelligent　Transportation　System　(ITS)　uses　traffic　data　gathered　by　crowdsensing　technology,　which　can　easily　get　vast　amounts　of　data　from　ordinary　people＇s　mobile　devices,　to　ease　congestion.　However,　crowdsensing　also　highlights　the　problem　that　the　abnormal　data,　which　we　often　call　as　outliers,　may　be　collected　for　analyzing　and　then　decrease　the　performances　of　ITS.　To　deal　with　this　problem,　we　propose　a　new　outlier　detection　algorithm　based　on　Kernel　Density　Estimation　(KDE)　in　this　paper.　Firstly,　an　optimal　estimation　of　the　traffic　data＇s　probability　density　function　(PDF)　is　acquired　by　KDE.　Then　a　belief　function,　which　is　determined　by　PDF,　is　built　to　detect　the　outliers　in　the　dataset.　Simulation　results　indicate　that,　compared　with　the　traditional　outlier　detection　algorithm　in　ITS,　our　algorithm　achieves　higher　detection　rate　and　lower　false　detection　rate.　©　2016　IEEE.