Graph neural network for traffic forecasting: A survey
Traffic forecasting is important for the success of intelligent transportation systems. Deep
learning models, including convolution neural networks and recurrent neural networks, have …
learning models, including convolution neural networks and recurrent neural networks, have …
On the opportunities and challenges of foundation models for geospatial artificial intelligence
Large pre-trained models, also known as foundation models (FMs), are trained in a task-
agnostic manner on large-scale data and can be adapted to a wide range of downstream …
agnostic manner on large-scale data and can be adapted to a wide range of downstream …
[HTML][HTML] A review of spatially-explicit GeoAI applications in Urban Geography
Urban Geography studies forms, social fabrics, and economic structures of cities from a
geographic perspective. Catalysed by the increasingly abundant spatial big data, Urban …
geographic perspective. Catalysed by the increasingly abundant spatial big data, Urban …
A survey on deep learning for human mobility
The study of human mobility is crucial due to its impact on several aspects of our society,
such as disease spreading, urban planning, well-being, pollution, and more. The …
such as disease spreading, urban planning, well-being, pollution, and more. The …
Dynamic graph convolutional recurrent imputation network for spatiotemporal traffic missing data
In real-world intelligent transportation systems, the spatiotemporal traffic data collected from
sensors often exhibit missing or corrupted data, significantly hindering the development of …
sensors often exhibit missing or corrupted data, significantly hindering the development of …
Dual-graph attention convolution network for 3-D point cloud classification
Three-dimensional point cloud classification is fundamental but still challenging in 3-D
vision. Existing graph-based deep learning methods fail to learn both low-level extrinsic and …
vision. Existing graph-based deep learning methods fail to learn both low-level extrinsic and …
Accurate solar PV power prediction interval method based on frequency-domain decomposition and LSTM model
The stability operation and real-time control of the integrated energy system with distributed
energy resources determines the higher and higher requirements for the accuracy of solar …
energy resources determines the higher and higher requirements for the accuracy of solar …
Integration of dockless bike-sharing and metro: Prediction and explanation at origin-destination level
Dockless bike-sharing is an effective solution for the metro's first-and last-mile connections.
To create a more bicycle-friendly environment, there is a need to accurately predict the use …
To create a more bicycle-friendly environment, there is a need to accurately predict the use …
ST-LBAGAN: Spatio-temporal learnable bidirectional attention generative adversarial networks for missing traffic data imputation
B Yang, Y Kang, YY Yuan, X Huang, H Li - Knowledge-Based Systems, 2021 - Elsevier
Real-time, accurate and comprehensive traffic flow data is the key of intelligent
transportation systems to provide efficient services for urban transportation. In the process of …
transportation systems to provide efficient services for urban transportation. In the process of …
ConvGCN-RF: A hybrid learning model for commuting flow prediction considering geographical semantics and neighborhood effects
Commuting flow prediction is a crucial issue for transport optimization and urban planning.
However, the two existing types of solutions have inherent flaws. One is traditional models …
However, the two existing types of solutions have inherent flaws. One is traditional models …