Developing a time-series speed prediction model using Transformer networks for freeway interchange areas
L Wu, Y Wang, J Liu, D Shan - Computers and Electrical Engineering, 2023 - Elsevier
This study investigates the lane-level speed distribution in the freeway interchange area and
develops a time-series speed prediction model using Transformer networks. The full-sample …
develops a time-series speed prediction model using Transformer networks. The full-sample …
Simre: Simple contrastive learning with soft logical rule for knowledge graph embedding
D Zhang, Z Rong, C Xue, G Li - Information Sciences, 2024 - Elsevier
Abstract Knowledge graphs serve as a pivotal framework for the structured representation of
information regarding entities and relations. However, in the real world, these knowledge …
information regarding entities and relations. However, in the real world, these knowledge …
Multi-view fusion neural network for traffic demand prediction
D Zhang, J Li - Information Sciences, 2023 - Elsevier
The extraction of spatial-temporal features is a crucial research in transportation studies, and
current studies typically use a unified temporal modeling mechanism and fixed spatial graph …
current studies typically use a unified temporal modeling mechanism and fixed spatial graph …
ISTGCN: Integrated spatio-temporal modeling for traffic prediction using traffic graph convolution network
To effectively estimate traffic patterns, spatial-temporal information must consider the
complex spatial connections on road networks and time-dependent traffic information …
complex spatial connections on road networks and time-dependent traffic information …
Dynamic spatiotemporal correlation graph convolutional network for traffic speed prediction
C Cao, Y Bao, Q Shi, Q Shen - Symmetry, 2024 - mdpi.com
Accurate and real-time traffic speed prediction remains challenging due to the irregularity
and asymmetry of real-traffic road networks. Existing models based on graph convolutional …
and asymmetry of real-traffic road networks. Existing models based on graph convolutional …
Enhancing Graph Convolutional Networks with Progressive Granular Ball Sampling Fusion: A Novel Approach to Efficient and Accurate GCN Training
H Cong, Q Sun, X Yang, K Liu, Y Qian - Information Sciences, 2024 - Elsevier
Graph convolutional network (GCN) has gained considerable attention and has been widely
utilized in graph data analytics. However, training large GCNs presents considerable …
utilized in graph data analytics. However, training large GCNs presents considerable …
STG4Traffic: A Survey and Benchmark of Spatial-Temporal Graph Neural Networks for Traffic Prediction
X Luo, C Zhu, D Zhang, Q Li - arXiv preprint arXiv:2307.00495, 2023 - arxiv.org
Traffic prediction has been an active research topic in the domain of spatial-temporal data
mining. Accurate real-time traffic prediction is essential to improve the safety, stability, and …
mining. Accurate real-time traffic prediction is essential to improve the safety, stability, and …
Incorporating environmental knowledge embedding and spatial-temporal graph attention networks for inland vessel traffic flow prediction
C Huang, D Chen, T Fan, B Wu, X Yan - Engineering Applications of …, 2024 - Elsevier
Accurate prediction of vessel traffic flow is crucial for maritime regulatory authorities and
transportation planners. However, existing methods for inland vessel traffic flow prediction …
transportation planners. However, existing methods for inland vessel traffic flow prediction …
Di-GraphGAN: An Enhanced Adversarial Learning Framework for Accurate Spatial-Temporal Traffic Forecasting Under Data Missing Scenarios
Nowadays, various disturbances in urban transportation data acquisition/processing/storage
lead to the inevitable data missing problem, which undermines the valuable traffic …
lead to the inevitable data missing problem, which undermines the valuable traffic …
Interactive dynamic diffusion graph convolutional network for traffic flow prediction
S Zhang, W Yu, W Zhang - Information Sciences, 2024 - Elsevier
Capturing the temporal and spatial features, and the spatiotemporal correlations in traffic
networks is the essential task for accurate traffic flow prediction. Although most existing …
networks is the essential task for accurate traffic flow prediction. Although most existing …