  Traffic signal control is one of the most effective methods of traffic
management in urban areas. In recent years, traffic control methods based on
deep reinforcement learning (DRL) have gained attention due to their ability to
exploit real-time traffic data, which is often poorly used by the traditional
hand-crafted methods. While most recent DRL-based methods have focused on
maximizing the throughput or minimizing the average travel time of the
vehicles, the fairness of the traffic signal controllers has often been
neglected. This is particularly important as neglecting fairness can lead to
situations where some vehicles experience extreme waiting times, or where the
throughput of a particular traffic flow is highly impacted by the fluctuations
of another conflicting flow at the intersection. In order to address these
issues, we introduce two notions of fairness: delay-based and throughput-based
fairness, which correspond to the two issues mentioned above. Furthermore, we
propose two DRL-based traffic signal control methods for implementing these
fairness notions, that can achieve a high throughput as well. We evaluate the
performance of our proposed methods using three traffic arrival distributions,
and find that our methods outperform the baselines in the tested scenarios.

Machine Learning;Artificial Intelligence