Bootstrapping on Continuous-Time Dynamic Graphs for Crowd Flow Modeling-Reference-Cited by-同舟云学术

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Bootstrapping on Continuous-Time Dynamic Graphs for Crowd Flow Modeling

Published:2024-10 Issue:10 Volume:36 Page:5039-5052
ISSN:1041-4347
Container-title:IEEE Transactions on Knowledge and Data Engineering
language:
Short-container-title:IEEE Trans. Knowl. Data Eng.

Author:

Xu Yi¹^ORCID,Han Liangzhe¹^ORCID,Sun Leilei¹^ORCID,Du Bowen¹^ORCID,Liu Chuanren²^ORCID,Xiong Hui³^ORCID

Affiliation:

1. State Key Laboratory of Complex and Critical Software Environment (CCSE), Beihang University, Beijing, China

2. Department of Business Analytics and Statistics, University of Tennessee, Knoxville, TN, USA

3. Hong Kong University of Science and Technology (Guangzhou) Thrust of Artificial Intelligence Nansha, Guangzhou, China

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/69/10679113/10487102.pdf?arnumber=10487102

Reference55 articles.

1. Diffusion convolutional recurrent neural network: Data-driven traffic forecasting;Li

2. Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting

3. Graph WaveNet for Deep Spatial-Temporal Graph Modeling

4. Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks

5. Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting

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