A Novel STFSA-CNN-GRU Hybrid Model for Short-Term Traffic Speed Prediction-Reference-Cited by-同舟云学术

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A Novel STFSA-CNN-GRU Hybrid Model for Short-Term Traffic Speed Prediction

Published:2023-04 Issue:4 Volume:24 Page:3728-3737
ISSN:1524-9050
Container-title:IEEE Transactions on Intelligent Transportation Systems
language:
Short-container-title:IEEE Trans. Intell. Transport. Syst.

Author:

Ma Changxi¹^ORCID,Zhao Yongpeng¹,Dai Guowen¹,Xu Xuecai²^ORCID,Wong Sze-Chun³

Affiliation:

1. School of Traffic and Transportation Engineering, Lanzhou Jiaotong University, Lanzhou, China

2. School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan, China

3. Department of Civil Engineering, The University of Hong Kong, Hong Kong

Funder

National Natural Science Foundation of China

Program of Humanities and Social Science of the Education Ministry of China

Foundation of A Hundred Youth Talents Training Program of Lanzhou Jiaotong University

“Double-First Class” Major Research Programs, Educational Department of Gansu Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,Mechanical Engineering,Automotive Engineering

Link

http://xplorestaging.ieee.org/ielx7/6979/10086684/09701439.pdf?arnumber=9701439

Reference28 articles.

1. Evaluation of Short-Term Freeway Speed Prediction Based on Periodic Analysis Using Statistical Models and Machine Learning Models

2. Short-Term Speed Prediction Using Remote Microwave Sensor Data: Machine Learning versus Statistical Model

3. Spatiotemporal Patterns in Large-Scale Traffic Speed Prediction

4. Traffic Speed Prediction Under Non-Recurrent Congestion: Based on LSTM Method and BeiDou Navigation Satellite System Data

5. Short-Term Traffic Flow Prediction Method for Urban Road Sections Based on Space–Time Analysis and GRU

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