Context Aware Dynamic Traffic Signal Optimization
This work addresses traffic congestion in urban areas by introducing fairness into signal control, though it appears incremental as it builds on existing real-time and forecasting methods.
The authors tackled the problem of urban traffic signal optimization by proposing an AI and context-aware system that incorporates fairness as a performance metric, aiming to reduce average waiting times while avoiding indefinite delays for other vehicles.
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.