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A hybrid neural network for large-scale expressway network OD prediction based on toll data
Accurate Origin-Destination (OD) prediction is significant for effective traffic monitor, which can support operation decision in traffic planning and management field. The enclosed expressway network system like toll gates system in China can collect mounts of trip records which can be gathered for...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6532916/ https://www.ncbi.nlm.nih.gov/pubmed/31120962 http://dx.doi.org/10.1371/journal.pone.0217241 |
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author | Fu, Xin Yang, Hao Liu, Chenxi Wang, Jianwei Wang, Yinhai |
author_facet | Fu, Xin Yang, Hao Liu, Chenxi Wang, Jianwei Wang, Yinhai |
author_sort | Fu, Xin |
collection | PubMed |
description | Accurate Origin-Destination (OD) prediction is significant for effective traffic monitor, which can support operation decision in traffic planning and management field. The enclosed expressway network system like toll gates system in China can collect mounts of trip records which can be gathered for OD prediction. The paper develops a novel neural network, which is named Expressway OD Prediction Neural Network (EODPNN) for toll data-based prediction. The network consists of the following three modules: The Feature Extension Module, the Memory Module, and the Prediction Module. In the process, the attributes data which can reflect the city attribute such as GDP, population, and the number of vehicles are considered to embeded into the notwork to increase the accuracy of the model. For the applicability improvment of the model, we categorize the cities in multiple classes based on their economy and population scales in this paper, which can provide a higher accurate prediction of OD by EODPNN. The results shows that, comparing to the traditional model like ARIMA and SVM, or typical neural networks like Bidirectional Long Short-term Memory, the EODPNN delivers a better prediction performance. The method proposed in this paper has been fully verified and has a potential to transplant to the other OD data-based management systems for a more accurate and flexible prediction. |
format | Online Article Text |
id | pubmed-6532916 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-65329162019-06-05 A hybrid neural network for large-scale expressway network OD prediction based on toll data Fu, Xin Yang, Hao Liu, Chenxi Wang, Jianwei Wang, Yinhai PLoS One Research Article Accurate Origin-Destination (OD) prediction is significant for effective traffic monitor, which can support operation decision in traffic planning and management field. The enclosed expressway network system like toll gates system in China can collect mounts of trip records which can be gathered for OD prediction. The paper develops a novel neural network, which is named Expressway OD Prediction Neural Network (EODPNN) for toll data-based prediction. The network consists of the following three modules: The Feature Extension Module, the Memory Module, and the Prediction Module. In the process, the attributes data which can reflect the city attribute such as GDP, population, and the number of vehicles are considered to embeded into the notwork to increase the accuracy of the model. For the applicability improvment of the model, we categorize the cities in multiple classes based on their economy and population scales in this paper, which can provide a higher accurate prediction of OD by EODPNN. The results shows that, comparing to the traditional model like ARIMA and SVM, or typical neural networks like Bidirectional Long Short-term Memory, the EODPNN delivers a better prediction performance. The method proposed in this paper has been fully verified and has a potential to transplant to the other OD data-based management systems for a more accurate and flexible prediction. Public Library of Science 2019-05-23 /pmc/articles/PMC6532916/ /pubmed/31120962 http://dx.doi.org/10.1371/journal.pone.0217241 Text en © 2019 Fu et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Fu, Xin Yang, Hao Liu, Chenxi Wang, Jianwei Wang, Yinhai A hybrid neural network for large-scale expressway network OD prediction based on toll data |
title | A hybrid neural network for large-scale expressway network OD prediction based on toll data |
title_full | A hybrid neural network for large-scale expressway network OD prediction based on toll data |
title_fullStr | A hybrid neural network for large-scale expressway network OD prediction based on toll data |
title_full_unstemmed | A hybrid neural network for large-scale expressway network OD prediction based on toll data |
title_short | A hybrid neural network for large-scale expressway network OD prediction based on toll data |
title_sort | hybrid neural network for large-scale expressway network od prediction based on toll data |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6532916/ https://www.ncbi.nlm.nih.gov/pubmed/31120962 http://dx.doi.org/10.1371/journal.pone.0217241 |
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