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Large-Scale Transportation Network Congestion Evolution Prediction Using Deep Learning Theory

Understanding how congestion at one location can cause ripples throughout large-scale transportation network is vital for transportation researchers and practitioners to pinpoint traffic bottlenecks for congestion mitigation. Traditional studies rely on either mathematical equations or simulation te...

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Detalles Bibliográficos
Autores principales: Ma, Xiaolei, Yu, Haiyang, Wang, Yunpeng, Wang, Yinhai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4363621/
https://www.ncbi.nlm.nih.gov/pubmed/25780910
http://dx.doi.org/10.1371/journal.pone.0119044
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author Ma, Xiaolei
Yu, Haiyang
Wang, Yunpeng
Wang, Yinhai
author_facet Ma, Xiaolei
Yu, Haiyang
Wang, Yunpeng
Wang, Yinhai
author_sort Ma, Xiaolei
collection PubMed
description Understanding how congestion at one location can cause ripples throughout large-scale transportation network is vital for transportation researchers and practitioners to pinpoint traffic bottlenecks for congestion mitigation. Traditional studies rely on either mathematical equations or simulation techniques to model traffic congestion dynamics. However, most of the approaches have limitations, largely due to unrealistic assumptions and cumbersome parameter calibration process. With the development of Intelligent Transportation Systems (ITS) and Internet of Things (IoT), transportation data become more and more ubiquitous. This triggers a series of data-driven research to investigate transportation phenomena. Among them, deep learning theory is considered one of the most promising techniques to tackle tremendous high-dimensional data. This study attempts to extend deep learning theory into large-scale transportation network analysis. A deep Restricted Boltzmann Machine and Recurrent Neural Network architecture is utilized to model and predict traffic congestion evolution based on Global Positioning System (GPS) data from taxi. A numerical study in Ningbo, China is conducted to validate the effectiveness and efficiency of the proposed method. Results show that the prediction accuracy can achieve as high as 88% within less than 6 minutes when the model is implemented in a Graphic Processing Unit (GPU)-based parallel computing environment. The predicted congestion evolution patterns can be visualized temporally and spatially through a map-based platform to identify the vulnerable links for proactive congestion mitigation.
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spelling pubmed-43636212015-03-23 Large-Scale Transportation Network Congestion Evolution Prediction Using Deep Learning Theory Ma, Xiaolei Yu, Haiyang Wang, Yunpeng Wang, Yinhai PLoS One Research Article Understanding how congestion at one location can cause ripples throughout large-scale transportation network is vital for transportation researchers and practitioners to pinpoint traffic bottlenecks for congestion mitigation. Traditional studies rely on either mathematical equations or simulation techniques to model traffic congestion dynamics. However, most of the approaches have limitations, largely due to unrealistic assumptions and cumbersome parameter calibration process. With the development of Intelligent Transportation Systems (ITS) and Internet of Things (IoT), transportation data become more and more ubiquitous. This triggers a series of data-driven research to investigate transportation phenomena. Among them, deep learning theory is considered one of the most promising techniques to tackle tremendous high-dimensional data. This study attempts to extend deep learning theory into large-scale transportation network analysis. A deep Restricted Boltzmann Machine and Recurrent Neural Network architecture is utilized to model and predict traffic congestion evolution based on Global Positioning System (GPS) data from taxi. A numerical study in Ningbo, China is conducted to validate the effectiveness and efficiency of the proposed method. Results show that the prediction accuracy can achieve as high as 88% within less than 6 minutes when the model is implemented in a Graphic Processing Unit (GPU)-based parallel computing environment. The predicted congestion evolution patterns can be visualized temporally and spatially through a map-based platform to identify the vulnerable links for proactive congestion mitigation. Public Library of Science 2015-03-17 /pmc/articles/PMC4363621/ /pubmed/25780910 http://dx.doi.org/10.1371/journal.pone.0119044 Text en © 2015 Ma 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, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Ma, Xiaolei
Yu, Haiyang
Wang, Yunpeng
Wang, Yinhai
Large-Scale Transportation Network Congestion Evolution Prediction Using Deep Learning Theory
title Large-Scale Transportation Network Congestion Evolution Prediction Using Deep Learning Theory
title_full Large-Scale Transportation Network Congestion Evolution Prediction Using Deep Learning Theory
title_fullStr Large-Scale Transportation Network Congestion Evolution Prediction Using Deep Learning Theory
title_full_unstemmed Large-Scale Transportation Network Congestion Evolution Prediction Using Deep Learning Theory
title_short Large-Scale Transportation Network Congestion Evolution Prediction Using Deep Learning Theory
title_sort large-scale transportation network congestion evolution prediction using deep learning theory
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4363621/
https://www.ncbi.nlm.nih.gov/pubmed/25780910
http://dx.doi.org/10.1371/journal.pone.0119044
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