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Improved genetic algorithm optimized LSTM model and its application in short-term traffic flow prediction
Considering that the road short-term traffic flow has strong time series correlation characteristics, a new long-term and short-term memory neural network (LSTM)-based prediction model optimized by the improved genetic algorithm (IGA) is proposed to improve the prediction accuracy of road traffic fl...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9454874/ https://www.ncbi.nlm.nih.gov/pubmed/36091988 http://dx.doi.org/10.7717/peerj-cs.1048 |
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author | Zhang, Junxi Qu, Shiru Zhang, Zhiteng Cheng, Shaokang |
author_facet | Zhang, Junxi Qu, Shiru Zhang, Zhiteng Cheng, Shaokang |
author_sort | Zhang, Junxi |
collection | PubMed |
description | Considering that the road short-term traffic flow has strong time series correlation characteristics, a new long-term and short-term memory neural network (LSTM)-based prediction model optimized by the improved genetic algorithm (IGA) is proposed to improve the prediction accuracy of road traffic flow. Firstly, an improved genetic algorithm (IGA) is proposed by dynamically adjusting the mutation rate and crossover rate of standard GA. Secondly, the parameters of the LSTM, such as the number of hidden units, training times, gradient threshold and learning rate, are optimized by the IGA. Therefore, the optimal parameters are obtained. In the analysis stage, 5-min short-term traffic flow data are used to demonstrate the superiority of the proposed method over the existing neural network algorithms. Finally, the results show that the Root Mean Square Error achieved by the proposed algorithm is lower than that achieved by the other neural network methods in both the weekday and weekend data sets. This verifies that the algorithm can adapt well to different kinds of data and achieve higher prediction accuracy. |
format | Online Article Text |
id | pubmed-9454874 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-94548742022-09-09 Improved genetic algorithm optimized LSTM model and its application in short-term traffic flow prediction Zhang, Junxi Qu, Shiru Zhang, Zhiteng Cheng, Shaokang PeerJ Comput Sci Adaptive and Self-Organizing Systems Considering that the road short-term traffic flow has strong time series correlation characteristics, a new long-term and short-term memory neural network (LSTM)-based prediction model optimized by the improved genetic algorithm (IGA) is proposed to improve the prediction accuracy of road traffic flow. Firstly, an improved genetic algorithm (IGA) is proposed by dynamically adjusting the mutation rate and crossover rate of standard GA. Secondly, the parameters of the LSTM, such as the number of hidden units, training times, gradient threshold and learning rate, are optimized by the IGA. Therefore, the optimal parameters are obtained. In the analysis stage, 5-min short-term traffic flow data are used to demonstrate the superiority of the proposed method over the existing neural network algorithms. Finally, the results show that the Root Mean Square Error achieved by the proposed algorithm is lower than that achieved by the other neural network methods in both the weekday and weekend data sets. This verifies that the algorithm can adapt well to different kinds of data and achieve higher prediction accuracy. PeerJ Inc. 2022-07-19 /pmc/articles/PMC9454874/ /pubmed/36091988 http://dx.doi.org/10.7717/peerj-cs.1048 Text en © 2022 Zhang et al. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by-nc/4.0/) , which permits using, remixing, and building upon the work non-commercially, as long as it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. |
spellingShingle | Adaptive and Self-Organizing Systems Zhang, Junxi Qu, Shiru Zhang, Zhiteng Cheng, Shaokang Improved genetic algorithm optimized LSTM model and its application in short-term traffic flow prediction |
title | Improved genetic algorithm optimized LSTM model and its application in short-term traffic flow prediction |
title_full | Improved genetic algorithm optimized LSTM model and its application in short-term traffic flow prediction |
title_fullStr | Improved genetic algorithm optimized LSTM model and its application in short-term traffic flow prediction |
title_full_unstemmed | Improved genetic algorithm optimized LSTM model and its application in short-term traffic flow prediction |
title_short | Improved genetic algorithm optimized LSTM model and its application in short-term traffic flow prediction |
title_sort | improved genetic algorithm optimized lstm model and its application in short-term traffic flow prediction |
topic | Adaptive and Self-Organizing Systems |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9454874/ https://www.ncbi.nlm.nih.gov/pubmed/36091988 http://dx.doi.org/10.7717/peerj-cs.1048 |
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