The　great　increase　in　car　ownership　has　led　to　the　daily　recurrence　of　traffic　congestion.　Thus,　traffic　mobility,　safety　and　emission　concerns　have　become　the　most　serious　challenges　for　transportation　researchers.　To　mitigate　traffic　congestion,　a　variety　of　proactive　traffic-control　strategies,　such　as　ramp　metering　(RM),　have　been　intensively　investigated　and　deployed.　With　the　aim　of　improving　freeway　traffic　conditions,　RM　regulates　the　on-ramp　flows　dynamically　in　response　to　dynamic　road　conditions.　However,　most　early　RM　strategies　focus　on　optimising　the　traffic　from　one　single　aspect.　This　paper　presents　an　RM　control　algorithm　that　predicts　and　evaluates　the　RM-controlled　future　traffic　states.　The　impact　of　RM　control　was　evaluated　using　a　macroscopic　traffic-flow　model.　The　designed　RM　control　algorithm　possesses　a　multi-objective　optimisation　module,　which　improves　the　traffic　network　from　the　aspects　of　mobility,　safety　and　emissions.　The　designed　algorithm　is　evaluated　through　simulation　and　calibrated　using　field　data　collected　over　an　11　km　major　freeway　stretch　in　Edmonton,　Alberta,　Canada.　The　comparison　of　the　proposed　algorithm-controlled　scenario　and　the　uncontrolled　scenario　shows　that　the　proposed　RM　control　algorithm　can　effectively　relieve　traffic　congestion,　improve　safety　and　reduce　carbon　emissions　concurrently.