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 …
Transformers in time series: A survey
Transformers have achieved superior performances in many tasks in natural language
processing and computer vision, which also triggered great interest in the time series …
processing and computer vision, which also triggered great interest in the time series …
Pdformer: Propagation delay-aware dynamic long-range transformer for traffic flow prediction
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 …
range of applications. The fundamental challenge in traffic flow prediction is to effectively …
Largest: A benchmark dataset for large-scale traffic forecasting
Road traffic forecasting plays a critical role in smart city initiatives and has experienced
significant advancements thanks to the power of deep learning in capturing non-linear …
significant advancements thanks to the power of deep learning in capturing non-linear …
Spatial-temporal fusion graph neural networks for traffic flow forecasting
Spatial-temporal data forecasting of traffic flow is a challenging task because of complicated
spatial dependencies and dynamical trends of temporal pattern between different roads …
spatial dependencies and dynamical trends of temporal pattern between different roads …
Dynamic graph convolutional recurrent network for traffic prediction: Benchmark and solution
Traffic prediction is the cornerstone of intelligent transportation system. Accurate traffic
forecasting is essential for the applications of smart cities, ie, intelligent traffic management …
forecasting is essential for the applications of smart cities, ie, intelligent traffic management …
[HTML][HTML] A multi-head attention-based transformer model for traffic flow forecasting with a comparative analysis to recurrent neural networks
S Reza, MC Ferreira, JJM Machado… - Expert Systems with …, 2022 - Elsevier
Traffic flow forecasting is an essential component of an intelligent transportation system to
mitigate congestion. Recurrent neural networks, particularly gated recurrent units and long …
mitigate congestion. Recurrent neural networks, particularly gated recurrent units and long …
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 …
A self-attention–based neural network for three-dimensional multivariate modeling and its skillful ENSO predictions
L Zhou, RH Zhang - Science Advances, 2023 - science.org
Large biases and uncertainties remain in real-time predictions of El Niño–Southern
Oscillation (ENSO) using process-based dynamical models; recent advances in data-driven …
Oscillation (ENSO) using process-based dynamical models; recent advances in data-driven …