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This study explores the integration of distributed acoustic sensing and deep learning for monitoring urban traffic dynamics with high spatiotemporal resolution. A deep learning framework is developed to analyze DAS data for vehicle detection and traffic state inference.
DAStatFormer is a hybrid multibranch Transformer that integrates statistical features with gated attention for efficient and accurate event classification in Distributed Acoustic Sensing (DAS), achieving up to 99.4% accuracy with significantly lower computational cost.