Short-term traffic flow prediction based on spatio-temporal analysis and CNN deep learning-Reference-Cited by-同舟云学术

Short-term traffic flow prediction based on spatio-temporal analysis and CNN deep learning

Published:2019-07-08 Issue:2 Volume:15 Page:1688-1711
ISSN:2324-9935
Container-title:Transportmetrica A: Transport Science
language:en
Short-container-title:Transportmetrica A: Transport Science

Author:

Zhang Weibin¹,Yu Yinghao¹,Qi Yong²,Shu Feng¹,Wang Yinhai³

Affiliation:

1. Nanjing University of Science and Technology, School of Electronic and Optical Engineering, Nangjing, People’s Republic of China

2. Nanjing University of Science and Technology, School of Computer Engineering, Nangjing, People’s Republic of China

3. Department of Civil and Environmental Engineering, Smart Transportation Applications and Research Laboratory, University of Washington, Washington, USA

Funder

Key projects of international scientific and technological innovation cooperation between governments

Publisher

Informa UK Limited

Subject

General Engineering,Transportation

Link

https://www.tandfonline.com/doi/pdf/10.1080/23249935.2019.1637966

Reference43 articles.

1. Short-Term Traffic Flow Prediction Using Neuro-Genetic Algorithms

2. Multistep-ahead River Flow Prediction using LS-SVR at Daily Scale

3. Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions

4. A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms

5. Du, S., T. Li, X. Gong, Z. Yu, and S. J. Horng. 2018. “A Hybrid Method for Traffic Flow Forecasting Using Multimodal Deep Learning.” arXiv preprint arXiv:1803.02099.

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