A survey on deep learning and its applications

S Dong, P Wang, K Abbas - Computer Science Review, 2021 - Elsevier
Deep learning, a branch of machine learning, is a frontier for artificial intelligence, aiming to
be closer to its primary goal—artificial intelligence. This paper mainly adopts the summary …

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 …

Pdformer: Propagation delay-aware dynamic long-range transformer for traffic flow prediction

J Jiang, C Han, WX Zhao, J Wang - … of the AAAI conference on artificial …, 2023 - ojs.aaai.org
As a core technology of Intelligent Transportation System, traffic flow prediction has a wide
range of applications. The fundamental challenge in traffic flow prediction is to effectively …

Simvp: Simpler yet better video prediction

Z Gao, C Tan, L Wu, SZ Li - … of the IEEE/CVF conference on …, 2022 - openaccess.thecvf.com
Abstract From CNN, RNN, to ViT, we have witnessed remarkable advancements in video
prediction, incorporating auxiliary inputs, elaborate neural architectures, and sophisticated …

Exploiting dynamic spatio-temporal graph convolutional neural networks for citywide traffic flows prediction

A Ali, Y Zhu, M Zakarya - Neural networks, 2022 - Elsevier
The prediction of crowd flows is an important urban computing issue whose purpose is to
predict the future number of incoming and outgoing people in regions. Measuring the …

Spatial-temporal graph ode networks for traffic flow forecasting

Z Fang, Q Long, G Song, K Xie - Proceedings of the 27th ACM SIGKDD …, 2021 - dl.acm.org
Spatial-temporal forecasting has attracted tremendous attention in a wide range of
applications, and traffic flow prediction is a canonical and typical example. The complex and …

Predrnn: A recurrent neural network for spatiotemporal predictive learning

Y Wang, H Wu, J Zhang, Z Gao, J Wang… - … on Pattern Analysis …, 2022 - ieeexplore.ieee.org
The predictive learning of spatiotemporal sequences aims to generate future images by
learning from the historical context, where the visual dynamics are believed to have modular …

Dynamic graph convolutional recurrent network for traffic prediction: Benchmark and solution

F Li, J Feng, H Yan, G Jin, F Yang, F Sun… - ACM Transactions on …, 2023 - dl.acm.org
Traffic prediction is the cornerstone of intelligent transportation system. Accurate traffic
forecasting is essential for the applications of smart cities, ie, intelligent traffic management …

Spatial-temporal synchronous graph convolutional networks: A new framework for spatial-temporal network data forecasting

C Song, Y Lin, S Guo, H Wan - Proceedings of the AAAI conference on …, 2020 - ojs.aaai.org
Spatial-temporal network data forecasting is of great importance in a huge amount of
applications for traffic management and urban planning. However, the underlying complex …

Spatio-temporal self-supervised learning for traffic flow prediction

J Ji, J Wang, C Huang, J Wu, B Xu, Z Wu… - Proceedings of the …, 2023 - ojs.aaai.org
Robust prediction of citywide traffic flows at different time periods plays a crucial role in
intelligent transportation systems. While previous work has made great efforts to model …