Proactive　traffic　control　based　on　macroscopic　traffic　flow　model　is　an　innovative　approach　to　active　traffic　management.　An　online,　model　predictive　control　(MPC)　based　active　traffic　control　algorithm,　DynaTAM,　is　proposed　to　implement　integrated　control　through　ramp　metering　(RM)　and　variable　speed　limit　(VSL).　DynaTAM　predicts　traffic　states　to　anticipate　incoming　traffic　congestion　and　to　provide　control　plan　recommendations　for　optimizing　the　network　traffic　conditions.　However,　as　with　other　sophisticated　prediction-based　control　algorithms,　a　system　fine-tuning　procedure　is　required　for　DynaTAM.　In　this　study,　two　aspects　will　be　addressed　to　further　improve　system　performance.　First,　the　control　algorithm　is　evaluated　to　find　the　correlations　between　the　prediction　horizon　length　and　the　controlled　system　performance　to　suggest　the　most　efficient　prediction　horizon　length　for　the　control　algorithm.　Second,　safety　considerations　are　quantitatively　incorporated　into　the　control　algorithm.　The　control　algorithm　optimizes　the　traffic　network　targeting　the　cost　reductions　achieved　by　both　improved　mobility　and　reduced　crash　risk.　A　field-data-based　simulation　study　is　conducted　to　evaluate　the　system　performance　within　various　parameters　and　to　determine　the　most　suitable　algorithm　parameters.　Optimized　by　the　refined　DynaTAM　algorithm,　the　targeted　area　shows　significant　improvements　in　terms　of　both　traffic　safety　and　mobility.