  Conventional urban traffic control systems have been based on historical
traffic data. Later advancements made use of detectors, which enabled the
gathering of real time traffic data, in order to reorganize and calibrate
traffic signalization programs. Further evolvement provided the ability to
forecast traffic conditions, in order to develop traffic signalization programs
and strategies precomputed and applied at the most appropriate time frame for
the optimal control of the current traffic conditions. We, propose the next
generation of traffic control systems based on principles of Artificial
Intelligence and Context Awareness. Most of the existing algorithms use average
waiting time or length of the queue to assess an algorithms performance.
However, a low average waiting time may come at the cost of delaying other
vehicles indefinitely. In our algorithm, besides the vehicle queue, we use
fairness also as an important performance metric to assess an algorithms
performance.
