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Found 6 papers, 2 papers with code
Towards Urban General Intelligence: A Review and Outlook of Urban Foundation Models
Machine learning techniques are now integral to the advancement of intelligent urban services, playing a crucial role in elevating the efficiency, sustainability, and livability of urban environments.
Machine Learning for Urban Air Quality Analytics: A Survey
In this article, we present a comprehensive survey of ML-based air quality analytics, following a roadmap spanning from data acquisition to pre-processing, and encompassing various analytical tasks such as pollution pattern mining, air quality inference, and forecasting.
Irregular Traffic Time Series Forecasting Based on Asynchronous Spatio-Temporal Graph Convolutional Network
Accurate traffic forecasting at intersections governed by intelligent traffic signals is critical for the advancement of an effective intelligent traffic signal control system.
Semantic-Fused Multi-Granularity Cross-City Traffic Prediction
Accurate traffic prediction is essential for effective urban management and the improvement of transportation efficiency.
Joint Air Quality and Weather Prediction Based on Multi-Adversarial Spatiotemporal Networks
Specifically, we first propose a heterogeneous recurrent graph neural network to model the spatiotemporal autocorrelation among air quality and weather monitoring stations.
HAR-Net:Fusing Deep Representation and Hand-crafted Features for Human Activity Recognition
This paper proposes a new approach based on deep learning and traditional feature engineering called HAR-Net to address the issue related to HAR.
