A Survey of Traffic Flow Prediction Methods Based on Long Short-Term Memory Networks-Reference-Cited by-同舟云学术

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A Survey of Traffic Flow Prediction Methods Based on Long Short-Term Memory Networks

Published:2024-09 Issue:5 Volume:16 Page:87-112
ISSN:1939-1390
Container-title:IEEE Intelligent Transportation Systems Magazine
language:
Short-container-title:IEEE Intell. Transport. Syst. Mag.

Author:

Ye Bao-Lin¹^ORCID,Zhang Mingjian¹,Li Lingxi²^ORCID,Liu Chunyuan¹,Wu Weimin³^ORCID

Affiliation:

1. School of Information Science and Engineering, Jiaxing University, Jiaxing, China

2. Elmore Family School of Electrical and Computer Engineering, Purdue University, Indianapolis, IN, USA

3. College of Control Science and Engineering, Zhejiang University, Hangzhou, China

Funder

Zhejiang Provincial Natural Science Foundation of China

Jiaxing Public Welfare Research Program

Science and Technology Program of Zhejiang Province of China

National Natural Science Foundation of China

State Key Laboratory of Industrial Control Technology

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/5117645/10666806/10562041.pdf?arnumber=10562041

Reference127 articles.

1. Research and Development of Intelligent Transportation Systems

2. Transportation 5.0: The DAO to Safe, Secure, and Sustainable Intelligent Transportation Systems

3. Multivariate Vehicular Traffic Flow Prediction: Evaluation of ARIMAX Modeling

4. Modeling and Forecasting Vehicular Traffic Flow as a Seasonal ARIMA Process: Theoretical Basis and Empirical Results

5. Combining kohonen maps with arima time series models to forecast traffic flow

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