The article presents a study on improving speed predictions in Intelligent Transportation Systems (ITS) using sparse GPS data and topographical features. The researchers aimed to extract accurate traffic insights from…
Continue readingThis article presents two frameworks for combating issues with fine-grained vehicle recognition (FGVR) caused by image noise. The first, a progressive multi-task anti-noise learning (PMAL) framework, improves recognition accuracy by…
Continue readingThe study introduces two frameworks, the Progressive Multi-task Anti-noise Learning (PMAL) and Progressive Multi-task Distilling (PMD), to tackle the intra-class variation issue in Fine-grained Vehicle Recognition (FGVR) caused by image…
Continue readingThe authors propose a Hybrid Time-Varying Graph Neural Network (HTVGNN) to improve traffic flow predictions. Traditional methods based on graph neural networks (GNNs) have limitations in capturing spatial correlations in…
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