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 …

Traffic prediction using artificial intelligence: Review of recent advances and emerging opportunities

M Shaygan, C Meese, W Li, XG Zhao… - … research part C: emerging …, 2022 - Elsevier
Traffic prediction plays a crucial role in alleviating traffic congestion which represents a
critical problem globally, resulting in negative consequences such as lost hours of additional …

Spatio-temporal graph neural networks for predictive learning in urban computing: A survey

G Jin, Y Liang, Y Fang, Z Shao, J Huang… - … on Knowledge and …, 2023 - ieeexplore.ieee.org
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 …

Deciphering spatio-temporal graph forecasting: A causal lens and treatment

Y Xia, Y Liang, H Wen, X Liu, K Wang… - Advances in …, 2024 - proceedings.neurips.cc
Abstract Spatio-Temporal Graph (STG) forecasting is a fundamental task in many real-world
applications. Spatio-Temporal Graph Neural Networks have emerged as the most popular …

Spatial-temporal graph neural network for traffic forecasting: An overview and open research issues

KHN Bui, J Cho, H Yi - Applied Intelligence, 2022 - Springer
Traffic forecasting plays an important role of modern Intelligent Transportation Systems (ITS).
With the recent rapid advancement in deep learning, graph neural networks (GNNs) have …

Large models for time series and spatio-temporal data: A survey and outlook

M Jin, Q Wen, Y Liang, C Zhang, S Xue, X Wang… - arXiv preprint arXiv …, 2023 - arxiv.org
Temporal data, notably time series and spatio-temporal data, are prevalent in real-world
applications. They capture dynamic system measurements and are produced in vast …

Meta-learning approaches for learning-to-learn in deep learning: A survey

Y Tian, X Zhao, W Huang - Neurocomputing, 2022 - Elsevier
Compared to traditional machine learning, deep learning can learn deeper abstract data
representation and understand scattered data properties. It has gained considerable …

[HTML][HTML] Traffic state estimation of urban road networks by multi-source data fusion: Review and new insights

J Xing, W Wu, Q Cheng, R Liu - Physica A: Statistical Mechanics and its …, 2022 - Elsevier
Accurate traffic state (ie, flow, speed, density, etc.) on an urban road network is important
information for urban traffic control and management strategies. However, due to the …

Unist: a prompt-empowered universal model for urban spatio-temporal prediction

Y Yuan, J Ding, J Feng, D Jin, Y Li - Proceedings of the 30th ACM …, 2024 - dl.acm.org
Urban spatio-temporal prediction is crucial for informed decision-making, such as traffic
management, resource optimization, and emergence response. Despite remarkable …

A temporal-aware LSTM enhanced by loss-switch mechanism for traffic flow forecasting

H Lu, Z Ge, Y Song, D Jiang, T Zhou, J Qin - Neurocomputing, 2021 - Elsevier
Short-term traffic flow forecasting at isolated points is a fundamental yet challenging task in
many intelligent transportation systems. We present a novel long short-term memory (LSTM) …