Deep Learning Models for Dynamic Real-World Graphs and Road Networks

Dear Colleagues,

Recent advances in deep learning have transformed the analysis of large-scale transportation networks and graph-structured data. Modern transportation systems continuously generate massive volumes of dynamic and heterogeneous data from road networks, connected vehicles, sensors, mobile devices, and digital infrastructure. These developments create new opportunities for data-driven transportation planning, traffic management, mobility prediction, route optimization, and intelligent decision making. This Special Issue focuses on deep learning models and graph-based techniques for dynamic real-world graphs and road networks. It aims to bring together researchers and practitioners working on the development of scalable, efficient, and interpretable learning methods that address the challenges of evolving transportation systems. Particular attention is given to approaches that integrate graph neural networks, spatio-temporal learning, foundation models, and hybrid AI techniques with real-world transportation applications.

Topics of interest:

• Graph neural networks for road networks and transportation systems.
• Dynamic and temporal graph learning for evolving transportation networks.
• Spatio-temporal deep learning for traffic forecasting and mobility prediction.
• Representation learning and embedding techniques for large-scale road networks.
• Deep learning for route planning, routing optimization, and navigation.
• Personalized and context-aware transportation recommendations.
• Foundation models and large-scale pre-trained models for transportation data.
• Self-supervised, transfer, and few-shot learning on transportation networks.
• Multimodal learning from trajectories, road networks, sensors, and geospatial data.
• Explainable, trustworthy, and uncertainty-aware AI for transportation applications.
• Reinforcement learning and decision-making for transportation management.
• Deep learning for intelligent transportation systems and connected mobility.

Dr. Muhammad Farhan
Dr. Henning Koehler
Guest Editors

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