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 …
Long sequence time-series forecasting with deep learning: A survey
The development of deep learning technology has brought great improvements to the field
of time series forecasting. Short sequence time-series forecasting no longer satisfies the …
of time series forecasting. Short sequence time-series forecasting no longer satisfies the …
Deep learning on traffic prediction: Methods, analysis, and future directions
X Yin, G Wu, J Wei, Y Shen, H Qi… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Traffic prediction plays an essential role in intelligent transportation system. Accurate traffic
prediction can assist route planing, guide vehicle dispatching, and mitigate traffic …
prediction can assist route planing, guide vehicle dispatching, and mitigate traffic …
Traffic transformer: Capturing the continuity and periodicity of time series for traffic forecasting
Traffic forecasting is a challenging problem due to the complexity of jointly modeling spatio‐
temporal dependencies at different scales. Recently, several hybrid deep learning models …
temporal dependencies at different scales. Recently, several hybrid deep learning models …
Predicting hourly PM2. 5 concentrations in wildfire-prone areas using a SpatioTemporal Transformer model
Globally, wildfires are becoming more frequent and destructive, generating a significant
amount of smoke that can transport thousands of miles. Therefore, improving air pollution …
amount of smoke that can transport thousands of miles. Therefore, improving air pollution …
Regularized graph structure learning with semantic knowledge for multi-variates time-series forecasting
Multivariate time-series forecasting is a critical task for many applications, and graph time-
series network is widely studied due to its capability to capture the spatial-temporal …
series network is widely studied due to its capability to capture the spatial-temporal …
Graph signal processing and deep learning: Convolution, pooling, and topology
Deep learning, particularly convolutional neural networks (CNNs), has yielded rapid,
significant improvements in computer vision and related domains. But conventional deep …
significant improvements in computer vision and related domains. But conventional deep …
A graph and attentive multi-path convolutional network for traffic prediction
Traffic prediction is an important and yet highly challenging problem due to the complexity
and constantly changing nature of traffic systems. To address the challenges, we propose a …
and constantly changing nature of traffic systems. To address the challenges, we propose a …
Spatio-temporal graph neural networks: A survey
Graph Neural Networks have gained huge interest in the past few years. These powerful
algorithms expanded deep learning models to non-Euclidean space and were able to …
algorithms expanded deep learning models to non-Euclidean space and were able to …
High-dimensional population inflow time series forecasting via an interpretable hierarchical transformer
Mobile device location data (MDLD) are emerging data sources in the transportation domain
that contain large-scale, fine-grained information on population inflow. However, limited …
that contain large-scale, fine-grained information on population inflow. However, limited …