Modern　proactive　control　algorithms　can　perform　real-time　traffic　state　prediction　and　help　determine　control　plans　for　active　traffic　control　measures,　such　as　ramp　metering　(RM)　and　variable　speed　limit　(VSL).　In　this　study,　a　control　algorithm,　DynaTAM　(Dynamic　Analysis　Tool　for　Active　Traffic　Demand　Management),　is　proposed　for　prediction-based,　integrated　optimization　control.　DynaTAM　considers　the　correlations　between　the　implemented　control　measures　and　determines　the　control　plan　for　both　RM　and　VSL　at　the　same　stage　of　optimization.　The　algorithm　framework　possesses　a　candidate　control　plan　generator,　which　reduces　the　computational　load　of　future　optimizations　and　eliminates　much　of　the　uncertainty　in　the　system　performance.　A　field-data-based　simulation　study　with　different　control　scenarios　is　conducted　to　evaluate　the　performance　of　DynaTAM.　The　DynaTAM　control　algorithm　is　shown　to　not　only　effectively　perform　integrated　and　coordinated　control　using　both　RM　and　VSL,　but　also　significantly　improve　the　efficiency　of　traffic　operations　during　peak　hours.