As　distributed　parameter　systems,　dynamics　of　freeway　traffic　are　dominated　by　the　current　traffic　parameter　and　boundary　fluxes　from　upstream/downstream　sections　or　on/off　ramps.　The　difference　between　traffic　demand-supply　and　boundary　fluxes　actually　reflects　the　congestion　level　of　freeway　travel.　This　study　investigates　simultaneous　traffic　density　and　boundary　flux　estimation　with　data　extracted　from　on-road　detectors.　The　existing　studies　for　traffic　estimation　mainly　focus　on　the　traffic　parameters　(density,　velocity　etc.)　of　mainline　traffic　and　ignore　flux　fluctuations　at　boundary　sections　of　the　freeway.　The　authors　propose　a　stochastic　hybrid　traffic　flow　model　by　extending　the　cell　transmission　model　with　Markovian　multi-mode　switching.　A　novel　interacting　multiple　model　filtering　for　simultaneous　input　and　state　estimation　is　developed　for　discrete-time　Markovian　switching　systems　with　unknown　input.　A　freeway　segment　of　Interstate　80　East　(I-80E)　in　Berkeley,　Northern　California,　is　chosen　to　investigate　the　performance　of　the　developed　approach.　Traffic　data　is　obtained　from　the　performance　measurement　system.