Sinceitisdifficulttofitmeasuredparametersusingtheconventionaltrafficmodel,anewtrafficdensityandaveragespeedmodelisintroducedinthispaper.Todeterminetrafficmodelstructuresaccurately,amodelidentificationmethodforuncertainnonlinearsystemisdeveloped.Tosimplifyuncertainnonlinearproblem,thispaperpresentsanewrobustcriteriontoidentifythemulti-sectiontrafficmodelstructureoffreewayefficiently.Inthenewmodelidentificationcriterion,numericallyefficientU-Dfactorizationisusedtoavoidcomputingthedeterminantvaluesoftwocomplexmatrices.ByestimatingthevaluesofU-Dfactorofdatamatrix,boththeupperandlowerboundsofsystemuncertaintiesaredescribed.Thusamodelstructureidentificationalgorithmisproposed.Comparisonsbetweenidentificationoutputsandsimulationoutputsoftrafficstatesshowthatthetrafficstatescanbeaccuratelypredictedbymeansofthenewtrafficmodelsandthestructureidentificationcriterion.
