A systematic review of generative adversarial networks for traffic state prediction: Overview, taxonomy, and future prospects
In recent years, advances in deep learning have had a significant impact in the
transportation domain, notably through the use of generative adversarial networks (GAN). As …
transportation domain, notably through the use of generative adversarial networks (GAN). As …
Self-supervised pre-training for robust and generic spatial-temporal representations
Advancements in mobile sensing, data mining, and artificial intelligence have revolutionized
the collection and analysis of Human-generated Spatial-Temporal Data (HSTD), paving the …
the collection and analysis of Human-generated Spatial-Temporal Data (HSTD), paving the …
[PDF][PDF] Personalized federated learning for cross-city traffic prediction
Traffic prediction plays an important role in urban computing. However, many cities face data
scarcity due to low levels of urban development. Although many approaches transfer …
scarcity due to low levels of urban development. Although many approaches transfer …
AirRadar: Inferring Nationwide Air Quality in China with Deep Neural Networks
Monitoring real-time air quality is essential for safeguarding public health and fostering
social progress. However, the widespread deployment of air quality monitoring stations is …
social progress. However, the widespread deployment of air quality monitoring stations is …