Intelligent Computational Methods for Urban Traffic Management and Control

Dear Colleagues,

Intelligent computational methods are the foundation of modern urban traffic management and control. They form a synergistic ecosystem that transforms traditional traffic management into an intelligent, predictive, and adaptive process leading to data-driven and efficient solutions. This Special Issue on "Intelligent Computational Methods for Urban Traffic Management and Control" will aim at research on this dynamic and rapidly evolving domain. Some intelligent computational methods are presented below, but this list is not comprehensive as new research directions may emerge.

Machine learning and deep learning techniques, reinforcement learning, fuzzy logic systems, and evolutionary and swarm intelligence algorithms, including genetic algorithms, are some of the methods often applied to traffic management and control. Multi-agent and hybrid AI, Intelligent Transportation Systems (ITSs), Internet of Things, and vehicle-to-everything (V2X) communication are contemporary research areas regarding urban traffic. Simulation-based optimization using tools such as advanced traffic simulators allows for the safe testing of control strategies under diverse scenarios, while Intelligent Decision Support Systems (IDSSs) enhance operational decision-making.

The urban traffic domain is constantly developing, and so are the methods applied within it. The theoretical frameworks, along with their validation and practical applications, guarantee the maximum utilization of current technologies, enhancing the efficiency and reliability of urban traffic management and control.

Dr. Yordanka Boneva
Dr. Alexander Gegov
Dr. Boriana Vatchova
Guest Editors

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