A　model　based　on　the　random　forest　is　constructed　to　repair　the　missing　trade　times　in　ETC　transaction　data.　The　driving　speed　and　traffic　volume　characteristics　of　the　vehicles　in　the　ETC　transaction　data　are　analyzed,　while　the　driving　speed　of　the　missed　transaction　vehicles,　and　the　distance　of　the　road　section　where　they　are　located,　are　combined　as　input　features　to　repair　the　missing　transaction　time.　A　one-day　transaction　data　of　a　province　is　used　to　test.　The　analysis　results　show　that　the　random　forest　model　has　a　better　restoration　effect　and　has　a　smaller　mean　absolute　error　and　root　mean　square　error;　its　mean　absolute　error　is　2.71 s,　the　highest　accuracy　among　the　compared　methods,　and　the　data　are　more　accurate　and　complete　after　interpolation　using　the　random　forest　model.　This　paper　suggests　that　the　research　based　on　ETC　transaction　data　should　first　adopt　the　processing　method　in　this　paper　to　repair　the　missing　trade　time　in　the　transaction　data　to　improve　the　integrity　of　the　data　used　and　ensure　the　validity　and　accuracy　of　the　relevant　calculation　results.　©　2023,　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.