Graph Construction for Traffic Prediction: A Data-Driven Approach-Reference-Cited by-同舟云学术

Graph Construction for Traffic Prediction: A Data-Driven Approach

Published:2022-09 Issue:9 Volume:23 Page:15015-15027
ISSN:1524-9050
Container-title:IEEE Transactions on Intelligent Transportation Systems
language:
Short-container-title:IEEE Trans. Intell. Transport. Syst.

Author:

Yu James J. Q.¹^ORCID

Affiliation:

1. Department of Computer Science and Engineering, Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation, Southern University of Science and Technology, Shenzhen, China

Funder

Stable Support Plan Program of Shenzhen Natural Science Fund

Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,Mechanical Engineering,Automotive Engineering

Link

http://xplorestaging.ieee.org/ielx7/6979/9893028/09678135.pdf?arnumber=9678135

Reference37 articles.

1. T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction

2. Dynamic Spatial-Temporal Graph Convolutional Neural Networks for Traffic Forecasting

3. ST-GRAT: A Novel Spatio-temporal Graph Attention Networks for Accurately Forecasting Dynamically Changing Road Speed

4. Forecasting road traffic speeds by considering area-wide spatio-temporal dependencies based on a graph convolutional neural network (GCN)

5. Spatial-Temporal Graph Attention Networks: A Deep Learning Approach for Traffic Forecasting

Cited by 29 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Adaptive graph neural network for traffic flow prediction considering time variation;Expert Systems with Applications;2024-12

2. Causal intervention graph neural network for fault diagnosis of complex industrial processes;Reliability Engineering & System Safety;2024-11

3. Parallel framework of a multi-graph convolutional network and gated recurrent unit for spatial–temporal metro passenger flow prediction;Expert Systems with Applications;2024-10

4. In-Depth Insights into the Application of Recurrent Neural Networks (RNNs) in Traffic Prediction: A Comprehensive Review;Algorithms;2024-09-06

5. A Wireless Signal Correlation Learning Framework for Accurate and Robust Multi-Modal Sensing;IEEE Journal on Selected Areas in Communications;2024-09