Artificial Intelligence for Traffic Understanding and Control
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".
Deadline for manuscript submissions: 15 February 2026 | Viewed by 36
Special Issue Editors
Interests: urban mobility; artificial intelligence
Special Issue Information
Dear Colleagues,
The rapid advancement of data-driven techniques and artificial intelligence (AI) has fundamentally transformed how we understand, model, and control urban transportation systems. AI methods continue to evolve, driven by breakthroughs in deep learning architectures, generative modeling, and learning-based control optimization. For instance, diffusion-based generative models introduce a new paradigm of data generation compared to traditional approaches, such as variational autoencoders (VAEs) and generative adversarial networks (GANs), and have shown promise in tasks such as vehicle trajectory generation. Meanwhile, large language models (LLMs) have emerged as powerful foundation models with strong reasoning and generalization capabilities, offering new possibilities for traffic analysis, forecasting, and control. These innovations present significant opportunities to address the inherent complexity and dynamism of modern transportation systems. Accordingly, there is substantial potential for further research into how such advanced AI techniques can be effectively harnessed to improve the efficiency, adaptability, and intelligence of intelligent transportation systems
This Special Issue will highlight recent advances and emerging trends in the application of data science and AI technologies to improve traffic understanding and control, particularly regarding strategies for developing more advanced intelligent transportation systems (ITSs). Original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:
- Deep generative models for traffic generation;
- Deep learning-based traffic forecasting;
- Reinforcement learning based traffic control and management;
- Data-driven traffic understanding;
- Data-driven traffic modeling and simulation;
- Large language models for traffic generation;
- Large language models for traffic analysis;
- Large language models for traffic control and management.
Dr. Jinyu Chen
Dr. Jiawei Wang
Guest Editors
Manuscript Submission Information
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Keywords
- intelligent transporation systems
- reinforcement learning
- deep learning
- large language models
- generative models
- urban computing
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