As　one　of　the　largest　Internet　of　Things　systems　in　the　world,　China＇s　expressway　electronic　toll　collection　(ETC)　generates　nearly　one　billion　pieces　of　transaction　data　every　day,　recording　the　traffic　trajectories　of　almost　all　vehicles　on　the　expressway,　which　has　great　potential　application　value.　However,　there　are　inevitable　missed　transactions　and　false　transactions　in　the　expressway　ETC　system,　which　leads　to　certain　false　and　missing　rates　in　ETC　data.　In　this　work,　a　dynamic　search　step　SegrDTW　algorithm　based　on　an　improved　DTW　algorithm　is　proposed　according　to　the　characteristics　of　expressway　ETC　data　with　origin-destination　(OD)　data　constraints　and　coupling　between　the　gantry　path　and　the　vehicle　trajectory.　Through　constructing　the　spatial　window　of　segment　retrieval,　the　spatial　complexity　of　the　DTW　algorithm　is　effectively　reduced,　and　the　efficiency　of　the　abnormal　ETC　data　detection　is　greatly　improved.　In　real　traffic　data　experiments,　the　SegrDTW　algorithm　only　needs　3.36　s　to　measure　the　abnormal　events　of　a　single　set　of　OD　path　data　for　10　days.　Compared　with　the　mainstream　algorithms,　the　SegrDTW　performs　best.　Therefore,　the　proposal　provides　a　feasible　method　for　the　abnormal　event　detection　of　expressway　ETC　data　in　a　province　and　even　the　whole　country.