This　paper　focuses　on　support　vector　machine　(SVM)　based　trip　chain＇s　activity　type　recognition.　First,　the　time-series　location　information　of　person　trip　is　processed　to　obtain　the　trip　chain　elements　including　moving　processes　and　activities,　and　the　activity　options　are　extracted　from　the　geographic　information　system　(GIS)　around　the　activity　sites.　Second,　the　activity　features　are　drawn　from　spatio-temporal　factors　of　trip　chain　to　serve　as　the　input　feature　vector　of　classifier.　A　SVM　based　one-to-one　classifier　is　established　and　the　method　of　one-to-one　classifier　voting　is　adopted　to　decide　the　most　likely　activity　type　from　the　activity　options.　Finally,　the　classifiers　are　trained　with　simulation　data　based　on　the　Gaussian　radial　basis　(RBF)　kernel　function　and　the　multilayer　perception　(MLP)　kernel　function　respectively,　and　then　examined　by　cross　validation.　The　result　shows　that　in　the　one-to-one　classifying　scheme,　the　highest　and　lowest　right　recognition　rate　with　RBF　are　99　and　62,　and　the　corresponding　results　with　MLP　are　97　and　54,　respectively.　©　2010　China　Association　for　Science　and　Technology.　Electronic　version　published　by　Elsevier　Limited.　All　rights　reserved.