To　scientifically　and　effectively　evaluate　the　service　capacity　of　expressway　service　areas　(ESAs)　and　improve　the　management　level　of　ESAs,　we　propose　a　method　for　the　recognition　of　vehicles　entering　ESAs　(VeESAs)　and　estimation　of　vehicle　dwell　times　using　electronic　toll　collection　(ETC)　data.　First,　the　ETC　data　and　their　advantages　are　described　in　detail,　and　then　the　cleaning　rules　are　designed　according　to　the　characteristics　of　the　ETC　data.　Second,　we　established　feature　engineering　according　to　the　characteristics　of　VeESA　and　proposed　the　XGBoost-based　VeESA　recognition　(VR-XGBoost)　model.　Studied　the　driving　rules　in　depth,　we　constructed　a　kinematics-based　vehicle　dwell　time　estimation　(K-VDTE)　model.　The　field　validation　in　Part　A/B　of　Yangli　ESA　using　real　ETC　transaction　data　demonstrates　that　the　effectiveness　of　our　proposal　outperforms　the　current　state-of-the-art.　Specifically,　in　Part　A　and　Part　B,　the　recognition　accuracies　of　VR-XGBoost　are　95.9%　and　97.4%,　respectively,　the　mean　absolute　errors　(MAEs)　of　dwell　time　are　52　and　14　s,　respectively,　and　the　root　mean　square　errors　(RMSEs)　are　69　and　22　s,　respectively.　In　addition,　the　confidence　level　of　controlling　the　MAE　of　dwell　time　within　2　min　is　more　than　97%.　This　work　can　effectively　recognize　the　VeESA　and　accurately　estimate　the　dwell　time,　which　can　provide　a　reference　idea　and　theoretical　basis　for　the　service　capacity　evaluation　and　layout　optimization　of　the　ESA.　©　2022　by　the　authors.