Intelligent　connected　vehicle　trajectory　data　are　of　great　value　for　data　mining　applications　such　as　traffic　management　and　commercial　institutions.　However,　the　leakage　of　sensitive　trajectory　makes　the　user　hesitate　to　use　the　system　if　no　privacy-preserving　mechanism　is　adopted.　In　this　paper,　we　propose　a　privacy-preserving　mechanism　with　differential　privacy　called　RPTR,　which　protects　a　vehicle＇s　real-time　trajectory　data　release.　First,　RPTR　adopts　a　dynamic　sampling　method　to　process　the　trajectory　data　to　meet　the　application　load　and　practicability.　Meanwhile,　to　ensure　the　data　availability,　ensemble　Kalman　filter　based　on　users＇　position　transfer　probability　matrix　is　used　in　the　prediction　calculation.　Also,　we　construct　the　privacy　budget　allocation　method　based　on　regional　privacy　weight　to　provide　better　protection　for　regions　with　high　user　density.　Through　our　analysis　and　experiments,　RPTR　not　only　protects　the　privacy　of　real-time　trajectory　data　but　also　guarantees　the　data　availability.