Exploiting dynamic spatio-temporal graph convolutional neural networks for citywide traffic flows prediction-Reference-Cited by-同舟云学术

Exploiting dynamic spatio-temporal graph convolutional neural networks for citywide traffic flows prediction

Published:2022-01 Issue: Volume:145 Page:233-247
ISSN:0893-6080
Container-title:Neural Networks
language:en
Short-container-title:Neural Networks

Author:

Ali Ahmad^ORCID,Zhu Yanmin,Zakarya Muhammad^ORCID

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience

Reference73 articles.

1. Traffic flow prediction for road transportation networks with limited traffic data;Abadi;IEEE Transactions on Intelligent Transportation Systems,2015

2. Leveraging spatio-temporal patterns for predicting citywide traffic crowd flows using deep hybrid neural networks;Ali,2019

3. A data aggregation based approach to exploit dynamic spatio-temporal correlations for citywide crowd flows prediction in fog computing;Ali;Multimedia Tools and Applications,2021

4. Exploiting dynamic spatio-temporal correlations for citywide traffic flow prediction using attention based neural networks;Ali;Information Sciences,2021

5. An LSTM network for highway trajectory prediction;Altché,2017

Cited by 216 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. Adaptive Decision Spatio-temporal neural ODE for traffic flow forecasting with Multi-Kernel Temporal Dynamic Dilation Convolution;Neural Networks;2024-11

3. PLEASING: Exploring the historical and potential events for temporal knowledge graph reasoning;Neural Networks;2024-11

4. GNNctd: A graph neural network based on complicated temporal dependencies modeling for fashion trend prediction;Knowledge-Based Systems;2024-10

5. GAN-based self-supervised message passing graph representation learning;Expert Systems with Applications;2024-10