Urban traffic as one of the most important challenges in modern city life needs practically
effective and efficient solutions. Artificial intelligence methods have gained popularity for …

The problem of adaptive traffic signal control in the multi-intersection system has attracted
the attention of researchers. Among the existing methods, reinforcement learning has shown …

In this paper, we focus on computing a consistent traffic signal configuration at each junction
that optimizes multiple performance indices, ie, multi-objective traffic signal control. The multi …

Since 1965 when the fuzzy logic and fuzzy algebra were introduced by Lotfi Zadeh, the fuzzy
theory successfully found its applications in the wide range of subject fields. This is mainly …

Significant research has been carried out in the field of User Behavior Analysis, focused on
understanding, modeling and predicting past, present and future behaviors of users …

Multi-agent reinforcement learning (MARL) based methods for adaptive traffic signal control
(ATSC) have shown promising potentials to solve the heavy traffic problems. The existing …

As traffic congestion in cities becomes serious, intelligent traffic signal control has been
actively studied. Deep Q-Network (DQN), a representative deep reinforcement learning …

Multi-agent deep reinforcement learning (MDRL) is a popular choice for multi-intersection
traffic signal control, generating decentralized cooperative traffic signal strategies in specific …

At complex intersections, traffic congestion causes pollution and leads to accidents, high
commute times and many other problems. Correct traffic signal timing can help to reduce the …

Increased traffic in metropolitan territories has led to significant concerns, such as road
blockage, transportation delays, pollution level, fuel consumption, etc. Traffic light signals at …