A Survey on Modern Deep Neural Network for Traffic Prediction: Trends, Methods and Challenges-Reference-Cited by-同舟云学术

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A Survey on Modern Deep Neural Network for Traffic Prediction: Trends, Methods and Challenges

Published:2020 Issue: Volume: Page:1-1
ISSN:1041-4347
Container-title:IEEE Transactions on Knowledge and Data Engineering
language:
Short-container-title:IEEE Trans. Knowl. Data Eng.

Author:

Tedjopurnomo David Alexander,Bao Zhifeng,Zheng Baihua,Choudhury Farhana,Qin A. K.

Funder

Google

Australian Research Council

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computational Theory and Mathematics,Computer Science Applications,Information Systems

Link

http://xplorestaging.ieee.org/ielx7/69/4358933/09112608.pdf?arnumber=9112608

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1. STFGCN: Spatial–temporal fusion graph convolutional network for traffic prediction;Expert Systems with Applications;2024-12

2. A traffic-weather generative adversarial network for traffic flow prediction for road networks under bad weather;Engineering Applications of Artificial Intelligence;2024-11

3. Machine Learning for public transportation demand prediction: A Systematic Literature Review;Engineering Applications of Artificial Intelligence;2024-11

4. Are Graphs and GCNs necessary for short-term metro ridership forecasting?;Expert Systems with Applications;2024-11

5. DDGformer: Direction- and distance-aware graph transformer for traffic flow prediction;Knowledge-Based Systems;2024-10

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