Urban traffic congestion has become a serious issue, and improving the flow of traffic
through cities is critical for environmental, social and economic reasons. Improvements in …

Latest technological improvements increased the quality of transportation. New data-driven
approaches bring out a new research direction for all control-based systems, eg, in …

Traffic signal control is essential for transportation efficiency in road networks. It has been a
challenging problem because of the complexity in traffic dynamics. Conventional …

Traffic signal control is an important and challenging real-world problem, which aims to
minimize the travel time of vehicles by coordinating their movements at the road …

Increasingly available city data and advanced learning techniques have empowered people
to improve the efficiency of our city functions. Among them, improving urban transportation …

Inefficient traffic control may cause numerous problems such as traffic congestion and
energy waste. This paper proposes a novel multi-agent reinforcement learning method …

The field of multi-agent system (MAS) is an active area of research within artificial
intelligence, with an increasingly important impact in industrial and other real-world …

The majority of multi-agent system implementations aim to optimise agents' policies with
respect to a single objective, despite the fact that many real-world problem domains are …

A variety of Reinforcement Learning (RL) techniques blends with one or more techniques
from Evolutionary Computation (EC) resulting in hybrid methods classified according to their …

Adaptive traffic signal control (ATSC) is a promising technique to improve the efficiency of
signalized intersections, especially in the era of connected vehicles (CVs) when real-time …