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 …
Spatio-temporal graph neural networks for predictive learning in urban computing: A survey
With recent advances in sensing technologies, a myriad of spatio-temporal data has been
generated and recorded in smart cities. Forecasting the evolution patterns of spatio-temporal …
generated and recorded in smart cities. Forecasting the evolution patterns of spatio-temporal …
Spatio-temporal meta-graph learning for traffic forecasting
Traffic forecasting as a canonical task of multivariate time series forecasting has been a
significant research topic in AI community. To address the spatio-temporal heterogeneity …
significant research topic in AI community. To address the spatio-temporal heterogeneity …
Spatio-temporal adaptive embedding makes vanilla transformer sota for traffic forecasting
With the rapid development of the Intelligent Transportation System (ITS), accurate traffic
forecasting has emerged as a critical challenge. The key bottleneck lies in capturing the …
forecasting has emerged as a critical challenge. The key bottleneck lies in capturing the …
A survey on graph representation learning methods
Graph representation learning has been a very active research area in recent years. The
goal of graph representation learning is to generate graph representation vectors that …
goal of graph representation learning is to generate graph representation vectors that …
Spatio-temporal graph few-shot learning with cross-city knowledge transfer
Spatio-temporal graph learning is a key method for urban computing tasks, such as traffic
flow, taxi demand and air quality forecasting. Due to the high cost of data collection, some …
flow, taxi demand and air quality forecasting. Due to the high cost of data collection, some …
A graph convolutional stacked bidirectional unidirectional-LSTM neural network for metro ridership prediction
Timely precise metro ridership forecasting is helpful to reveal real-time traffic demand, which
is a crucial but challenging task in modern traffic management. Given the complex spatial …
is a crucial but challenging task in modern traffic management. Given the complex spatial …
Automated dilated spatio-temporal synchronous graph modeling for traffic prediction
Accurate traffic prediction is a challenging task in intelligent transportation systems because
of the complex spatio-temporal dependencies in transportation networks. Many existing …
of the complex spatio-temporal dependencies in transportation networks. Many existing …
Cross-and Context-Aware Attention Based Spatial-Temporal Graph Convolutional Networks for Human Mobility Prediction
The COVID-19 pandemic has dramatically transformed human mobility patterns. Therefore,
human mobility prediction for the “new normal” is crucial to infrastructure redesign …
human mobility prediction for the “new normal” is crucial to infrastructure redesign …
Traffic-GGNN: predicting traffic flow via attentional spatial-temporal gated graph neural networks
Y Wang, J Zheng, Y Du, C Huang… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Recent spatial-temporal graph-based deep learning methods for Traffic Flow Prediction
(TFP) problems have shown superior performance in modeling higher-level spatial …
(TFP) problems have shown superior performance in modeling higher-level spatial …