Graph neural network for traffic forecasting: A survey

W Jiang, J Luo - Expert systems with applications, 2022 - Elsevier
Traffic forecasting is important for the success of intelligent transportation systems. Deep
learning models, including convolution neural networks and recurrent neural networks, have …

Long sequence time-series forecasting with deep learning: A survey

Z Chen, M Ma, T Li, H Wang, C Li - Information Fusion, 2023 - Elsevier
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 …

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 …

Traffic transformer: Capturing the continuity and periodicity of time series for traffic forecasting

L Cai, K Janowicz, G Mai, B Yan, R Zhu - Transactions in GIS, 2020 - Wiley Online Library
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 …

Predicting hourly PM2. 5 concentrations in wildfire-prone areas using a SpatioTemporal Transformer model

M Yu, A Masrur, C Blaszczak-Boxe - Science of The Total Environment, 2023 - Elsevier
Globally, wildfires are becoming more frequent and destructive, generating a significant
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

H Yu, T Li, W Yu, J Li, Y Huang, L Wang… - arXiv preprint arXiv …, 2022 - arxiv.org
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 …

Graph signal processing and deep learning: Convolution, pooling, and topology

M Cheung, J Shi, O Wright, LY Jiang… - IEEE Signal …, 2020 - ieeexplore.ieee.org
Deep learning, particularly convolutional neural networks (CNNs), has yielded rapid,
significant improvements in computer vision and related domains. But conventional deep …

A graph and attentive multi-path convolutional network for traffic prediction

J Qi, Z Zhao, E Tanin, T Cui, N Nassir… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
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 …

Spatio-temporal graph neural networks: A survey

ZA Sahili, M Awad - arXiv preprint arXiv:2301.10569, 2023 - arxiv.org
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 …

High-dimensional population inflow time series forecasting via an interpretable hierarchical transformer

S Hu, C Xiong - Transportation research part C: emerging technologies, 2023 - Elsevier
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 …