Based　on　on-board　diagnostics　and　Global　Position　System　installed　in　taxicabs,　driver　behavior　data　is　collected.　Left　turn　data　on　six　similar　curves　are　extracted,　and　speed,　acceleration,　yaw　rate,　and　sideslip　angle　of　drivers　in　time　series　are　selected　as　clustering　indexes.　Initial　clustering　is　implemented　by　Dynamic　Time　Warping　(DTW)　and　Hierarchical　Clustering,　and　the　clustering　results　are　put　into　the　Hidden　Markov　Model　(HMM)　to　iteratively　optimize　the　results　for　achieving　convergence.　Driver　behavior　patterns　over　time　while　driving　on　the　curves　and　the　statistical　characteristics　of　different　groups　are　examined.　All　indexes　including　lateral　vehicle　control　and　longitudinal　vehicle　control　have　a　significant　difference　in　different　groups,　indicating　that　the　clustering　method　of　DTW　and　HMM　can　effectively　classify　driver　behavior.　Finally,　the　driving　behavior　in　different　groups　is　further　investigated　and　classified　based　on　characteristics　related　to　safe　and　ecological　driving.　This　method　can　be　applied　by　automobile　insurance　companies,　and　for　the　development　of　specific　training　courses　for　drivers　to　optimize　their　driving　behavior.