Cities today generate massive volumes of mobility data through sensors such as cameras, GPS, and roadside units. Traditional cloud-based systems struggle to process this data efficiently, particularly when real-time responsiveness is required. In a new AIoTwin blog post, Ivan Kralj explores how cloudlets, Edge AI, and adaptive learning are enabling scalable and accurate traffic prediction for smart cities.
This work originates from Ivan’s research exchange in Sweden and ongoing collaboration with RISE Research Institutes of Sweden.
Key Highlights:
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Traffic prediction is uniquely challenging due to the interconnected, dynamic nature of road networks.
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Fully centralized systems face limitations in scalability, latency, and reliability.
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Semi-decentralized training of Spatio-Temporal Graph Neural Networks (ST-GNNs) across distributed cloudlets improves resilience and efficiency.
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Sudden traffic events require specialized evaluation metrics — introducing Sudden Change in Speed Rate (SCSR) to detect congestion onset and recovery.
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Adaptive graph pruning reduces communication overhead while maintaining high prediction accuracy.
Ivan’s research demonstrates that decentralized ST-GNN training is both practical and essential for future smart mobility systems, offering a more responsive and bandwidth-efficient approach to traffic prediction.
Read the full post here.