AI-Driven Traffic Control and Management Systems for Smart Cities

Dear Colleagues,

The rapid urbanization and increasing vehicle ownership in cities worldwide have led to significant challenges in traffic management and transportation efficiency. Conventional traffic control systems often struggle to adapt to dynamic traffic conditions, resulting in congestion, frequent traffic accidents, increased travel times, and environmental pollution. In recent years, the integration of artificial intelligence (AI) technologies with traffic control and management systems has emerged as a promising solution to these challenges. Crucially, the real-world impact of these AI advancements is determined by their deployment within complex cyber–physical systems involving sensors, embedded devices, and communication networks. This integration offers unprecedented opportunities for developing intelligent, adaptive, and responsive transportation networks.

We invite authors to submit original and unpublished results utilizing AI-based approaches, such as Machine Learning (ML), Vision-Language Models (VLMs), Large Language Models (LLMs), Reinforcement Learning (RL), and other related cyber–physical methods, on topics including, but not limited to, the following:

• Traffic scene perception for driver state recognition and the behavior prediction of traffic participants;
• Methods for understanding and predicting traffic flows and traffic states;
• Decision-making, planning, control algorithms, and end-to-end approaches for autonomous vehicles;
• Embedded AI, edge computing, and hardware acceleration for real-time traffic control and autonomous driving systems;
• Intelligent transportation management systems for optimizing traffic networks;
• Multi-agent interaction mechanisms (human–AI and agent–agent) for traffic control and management systems;
• Traffic system modeling, simulation, and scenario generation techniques;
• Cyber–physical systems (CPSs) modeling, integration, and co-simulation in intelligent transportation;
• Real-world deployment and validation of AI-based traffic solutions using sensors, IoT devices, and vehicular networks.
• Safety, interpretability, and ethical frameworks for AI-based traffic control and management.

Prof. Dr. Chunyue Song
Prof. Dr. Shuyou Yu
Prof. Dr. Beiping Hou
Guest Editors

Manuscript Submission Information

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• intelligent transportation systems
• traffic scene perception
• intelligent decision
• intelligent vehicles
• human-ai systems
• multi-agent systems
• traffic simulation
• embedded systems
• cyber-physical systems
• machine learning
• vision-language models
• large language models
• reinforcement learning