UAV-Assisted Traffic Speed Prediction via Gray Relational Analysis and Deep Learning

Accurate traffic prediction is crucial to alleviating congestion in cities. Existing physical sensor-based data acquisition methods have high transmission costs, serious information redundancy, and large calculation volumes for spatiotemporal processing, thus making it difficult ensure accuracy real-time prediction. With the increasing resolution of UAV imagery, use unmanned aerial vehicles (UAV) imagery obtain has become a hot spot. Still, analyzing predicting status after extracting neglected. We develop framework speed extraction based on which consists two parts: module recognition deep learning. First, we learning automate road information, implement vehicle using convolutional neural networks calculate average sections panchromatic multispectral image matching construct dataset. Then, propose an attention-enhanced that considers characteristics increases weights key roads by important fine-grained features twice improve target roads. Finally, validate effectiveness proposed method real data. Compared with baseline algorithm, our algorithm achieves best performance regarding stability.