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Enhancing travel time prediction with deep learning on chronological and retrospective time order information of big traffic data

With growing environmental concerns and the exploitation of ubiquitous big data, smart transportation is transforming logistics business and operations into a more sustainable approach. To answer questions in intelligent transportation planning, such as which data are feasible, which methods are app...

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Autores principales: Chen, Claire Y. T., Sun, Edward W., Chang, Ming-Feng, Lin, Yi-Bing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10078079/
https://www.ncbi.nlm.nih.gov/pubmed/37361091
http://dx.doi.org/10.1007/s10479-023-05223-7
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author Chen, Claire Y. T.
Sun, Edward W.
Chang, Ming-Feng
Lin, Yi-Bing
author_facet Chen, Claire Y. T.
Sun, Edward W.
Chang, Ming-Feng
Lin, Yi-Bing
author_sort Chen, Claire Y. T.
collection PubMed
description With growing environmental concerns and the exploitation of ubiquitous big data, smart transportation is transforming logistics business and operations into a more sustainable approach. To answer questions in intelligent transportation planning, such as which data are feasible, which methods are applicable for intelligent prediction of such data, and what are the available operations for prediction, this paper offers a new deep learning approach called bi-directional isometric-gated recurrent unit (BDIGRU). It is merged to the deep learning framework of neural networks for predictive analysis of travel time and business adoption for route planning. The proposed new method directly learns high-level features from big traffic data and reconstructs them by its own attention mechanism drawn by temporal orders to complete the learning process recursively in an end-to-end manner. After deriving the computational algorithm with stochastic gradient descent, we use the proposed method to perform predictive analysis of stochastic travel time under various traffic conditions (especially for congestions) and then determine the optimal vehicle route with the shortest travel time under future uncertainty. Based on empirical results with big traffic data, we show that the proposed BDIGRU method can (1) significantly improve the predictive accuracy of one-step 30 min ahead travel time compared to several conventional (data-driven, model-driven, hybrid, and heuristics) methods measured with several performance criteria, and (2) efficiently determine the optimal vehicle route in relation to the predictive variability under uncertainty.
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spelling pubmed-100780792023-04-07 Enhancing travel time prediction with deep learning on chronological and retrospective time order information of big traffic data Chen, Claire Y. T. Sun, Edward W. Chang, Ming-Feng Lin, Yi-Bing Ann Oper Res Original Research With growing environmental concerns and the exploitation of ubiquitous big data, smart transportation is transforming logistics business and operations into a more sustainable approach. To answer questions in intelligent transportation planning, such as which data are feasible, which methods are applicable for intelligent prediction of such data, and what are the available operations for prediction, this paper offers a new deep learning approach called bi-directional isometric-gated recurrent unit (BDIGRU). It is merged to the deep learning framework of neural networks for predictive analysis of travel time and business adoption for route planning. The proposed new method directly learns high-level features from big traffic data and reconstructs them by its own attention mechanism drawn by temporal orders to complete the learning process recursively in an end-to-end manner. After deriving the computational algorithm with stochastic gradient descent, we use the proposed method to perform predictive analysis of stochastic travel time under various traffic conditions (especially for congestions) and then determine the optimal vehicle route with the shortest travel time under future uncertainty. Based on empirical results with big traffic data, we show that the proposed BDIGRU method can (1) significantly improve the predictive accuracy of one-step 30 min ahead travel time compared to several conventional (data-driven, model-driven, hybrid, and heuristics) methods measured with several performance criteria, and (2) efficiently determine the optimal vehicle route in relation to the predictive variability under uncertainty. Springer US 2023-04-06 /pmc/articles/PMC10078079/ /pubmed/37361091 http://dx.doi.org/10.1007/s10479-023-05223-7 Text en © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Original Research
Chen, Claire Y. T.
Sun, Edward W.
Chang, Ming-Feng
Lin, Yi-Bing
Enhancing travel time prediction with deep learning on chronological and retrospective time order information of big traffic data
title Enhancing travel time prediction with deep learning on chronological and retrospective time order information of big traffic data
title_full Enhancing travel time prediction with deep learning on chronological and retrospective time order information of big traffic data
title_fullStr Enhancing travel time prediction with deep learning on chronological and retrospective time order information of big traffic data
title_full_unstemmed Enhancing travel time prediction with deep learning on chronological and retrospective time order information of big traffic data
title_short Enhancing travel time prediction with deep learning on chronological and retrospective time order information of big traffic data
title_sort enhancing travel time prediction with deep learning on chronological and retrospective time order information of big traffic data
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10078079/
https://www.ncbi.nlm.nih.gov/pubmed/37361091
http://dx.doi.org/10.1007/s10479-023-05223-7
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