To　obtain　the　spatial　and　temporal　traffic　operation　parameters　of　a　road　network　more　accurately　using　toll　Origin-Destination　(OD)data,　this　paper　proposes　a　method　to　calculate　the　traffic　operation　status　by　combining　machine　learning　prediction　of　the　service　area　approach　with　incremental　iterations　of　toll　OD　data.　The　results　show　that　there　is　a　maximum　peak　value　for　the　minimum　probability　threshold　Pt　of　entering　the　service　area,　and　the　overall　prediction　accuracy　is　highest　when　Pt　is　approximately　0.9.　In　addition,　the　random　forest　method　shows　a　significant　accuracy　advantage　in　different　categories　of　toll　forms　and　vehicles.　The　OD　data　for　short-distance　trips　are　used　as　the　initial　value　to　project　the　travel　time　of　the　basic　road　section,　and　the　road　network　operation　status　is　calculated　incrementally　from　near　to　far　using　the　multiples　of　the　distance　threshold　Pt　as　the　time　interval.　The　OD　data　calculated　in　the　previous　iterations　are　weighted　more　heavily　than　the　data　in　the　later　iterations　to　complete　the　road　section　status　for　missing　data　in　the　spatio-temporal　dimension.　The　effectiveness　of　the　algorithm　was　tested　in　a　large-scale　provincial　real-world　road　network　environment　by　designing　four　different　sets　of　Pt　and　three　control　groups.　The　results　of　the　analysis　of　road　traffic　flow　and　travel　speed　show　that　the　smaller　the　Pt,　the　smaller　the　error.　The　error　is　minimized　at　Pt=5　km,　where　an　average　error　of　5.46%　for　traffic　flow　and　9.84%　for　speed,　using　the　random　forest　model.　Compared　with　the　nearest　neighbor　upstream　and　downstream　interpolation　method,　the　average　accuracy　of　the　proposed　method　for　calculating　traffic　flow　is　3.98%　higher,　and　the　average　accuracy　of　speed　is　4.33%　higher　than　that　of　the　average　travel　time　method.　The　average　accuracy　of　traffic　flow　is　5.2%　higher,　and　the　average　accuracy　of　speed　is　5.87%　higher.　The　test　results　show　high　accuracy　and　indicate　that　the　method　has　good　practicality.　©　2022,　Editorial　Department　of　China　Journal　of　Highway　and　Transport.　All　right　reserved.