In　this　study,　two　automated　statistical　methods　for　highway　freight　were　developed　to　improve　the　efficiency　and　accuracy　of　highway　freight　statistics　using　multisource　data　from　automated　highway　traffic　detection　stations,　axle　weight　detection,　freeway　charging,　probe　trucks,　and　state　freight　surveys.　Both　methods　are　based　on　the　data　fusion　principle　of　fixed　and　floating　detection.　Fixed　detection　and　floating　detection　were　used　to　obtain　the　freight　scale　and　freight　characteristics,　respectively.　Floating　detection　data　were　obtained　from　the　floating　trucks,　and　fixed　detection　data　were　obtained　from　highway　charging　and　automated　traffic　detection.　By　nested　computing　of　the　data　obtained　from　equipment　detection　and　manual　survey,　the　problem　that　the　floating　trucks　could　not　represent　the　freight　characteristics　of　each　category　of　trucks　in　the　hierarchical　statistics　was　solved.　Through　data　consensus　analysis,　the　inconsistent　definition　of　the　vehicle　type　in　data　fusion　caused　by　the　different　vehicle　type　classification　standards　for　different　sources　of　data　was　solved.　Compared　with　conventional　methods,　statistics　are　realized　in　the　automated　mode　instead　of　the　manual　mode.　Noncommercial　trucks　excluded　in　the　conventional　methods　were　included.　The　results　were　obtained　in　terms　of　the　area　where　the　freight　was　completed　instead　of　the　area　where　the　truck　was　registered.　The　statistical　cycle　was　shortened　from　one　month　to　one　day.　Furthermore,　the　specified　statistics　can　be　realized　in　terms　of　truck　category,　road　category,　transport　category,　and　highway　route.　The　highway　network　of　Liaoning　province　and　State　Highway　G102　were　selected　as　examples　to　provide　statistical　results.　Finally,　taking　the　freeway　network　as　the　test　area　and　the　statistical　result　with　freeway　charging　data　as　the　benchmark,　we　tested　the　accuracy　of　the　proposed　method　based　on　a　traffic　detection　station.　The　result　shows　that　the　error　in　tons　is　11.5%.　A　comparison　of　both　methods　reveals　that　the　method　based　on　traffic　detection　stations　has　higher　applicability　and　flexibility　than　methods　based　on　highway　charging　data.　These　methods　provide　a　foundation　for　constructing　the　modern　highway　statistical　system　of　China.　The　proposed　nested　analysis　method　of　equipment　detection　and　manual　survey　data　provides　a　reference　for　research　on　big　data　in　transportation.　©　2021,　Editorial　Department　of　China　Journal　of　Highway　and　Transport.　All　right　reserved.