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Urban highways have undergone a transformation in the recent years due to increased traffic demand. Optimal usage of the existing road transport infrastructure has become a priority. To ensure efficient usage of the existing traffic system capacity, intelligence needs to be added to its control systems. Such control solutions are from the domain of intelligent transportation systems (ITS). Mostly used solutions are ramp metering and variable speed limit control (VSLC). This paper presents a new learning based cooperative ramp metering strategy in which several well-known ramp metering strategies (ALIENA, SWARM, HELPER) are used to create a learning set and then create an ANFIS based cooperative ramp metering controller. Proposed urban highway control approach is tested in simulations using an augmented version of the CTMSIM simulator, traffic data for a typical working day and city of Zagreb bypass as case study. Obtained results are compared to standard ramp metering approaches and VSLC using travel time, Delay, and maximum vehicle queue length as quality measures.