Transformer-enhanced periodic temporal convolution network for long short-term traffic flow forecasting
Q Ren, Y Li, Y Liu - Expert Systems with Applications, 2023 - Elsevier
Abstract Recently, Temporal Convolution Networks (TCNs) and Graph Convolution Network
(GCN) have been developed for traffic forecasting and obtained promising results as their …
(GCN) have been developed for traffic forecasting and obtained promising results as their …
Stone: A spatio-temporal ood learning framework kills both spatial and temporal shifts
Traffic prediction is a crucial task in the Intelligent Transportation System (ITS), receiving
significant attention from both industry and academia. Numerous spatio-temporal graph …
significant attention from both industry and academia. Numerous spatio-temporal graph …
[HTML][HTML] RT-GCN: Gaussian-based spatiotemporal graph convolutional network for robust traffic prediction
Traffic forecasting plays a critical role in intelligent transportation systems (ITS) in smart
cities. Travelers as well as urban managers rely on reliable traffic information to make their …
cities. Travelers as well as urban managers rely on reliable traffic information to make their …
Frigate: Frugal spatio-temporal forecasting on road networks
Modelling spatio-temporal processes on road networks is a task of growing importance.
While significant progress has been made on develo** spatio-temporal graph neural …
While significant progress has been made on develo** spatio-temporal graph neural …
Towards Dynamic Spatial-Temporal Graph Learning: A Decoupled Perspective
With the progress of urban transportation systems, a significant amount of high-quality traffic
data is continuously collected through streaming manners, which has propelled the …
data is continuously collected through streaming manners, which has propelled the …
TTS-norm: Forecasting tensor time series via multi-way normalization
Tensor time series (TTS) data, a generalization of one-dimensional time series on a high-
dimensional space, is ubiquitous in real-world applications. Compared to modeling time …
dimensional space, is ubiquitous in real-world applications. Compared to modeling time …
Multi-Modality Spatio-Temporal Forecasting via Self-Supervised Learning
Multi-modality spatio-temporal (MoST) data extends spatio-temporal (ST) data by
incorporating multiple modalities, which is prevalent in monitoring systems, encompassing …
incorporating multiple modalities, which is prevalent in monitoring systems, encompassing …
Prediction of inbound and outbound passenger flow in urban rail transit based on spatio-temporal attention residual network
J Yang, X Dong, H Yang, X Han, Y Wang, J Chen - Applied Sciences, 2023 - mdpi.com
Passenger flow prediction is a critical approach to ensure the effective functioning of urban
rail transit. However, there are few studies that combine multiple influencing factors for short …
rail transit. However, there are few studies that combine multiple influencing factors for short …
Learning Gaussian mixture representations for tensor time series forecasting
Tensor time series (TTS) data, a generalization of one-dimensional time series on a high-
dimensional space, is ubiquitous in real-world scenarios, especially in monitoring systems …
dimensional space, is ubiquitous in real-world scenarios, especially in monitoring systems …
Graph dropout self-learning hierarchical graph convolution network for traffic prediction
Q Ni, W Peng, Y Zhu, R Ye - Engineering Applications of Artificial …, 2023 - Elsevier
Traffic prediction is a challenging topic in urban traffic construction and management due to
its complex dynamic spatial–temporal correlations. Currently, graph neural network …
its complex dynamic spatial–temporal correlations. Currently, graph neural network …