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Development of AI-Augmented optimization technique for analysis & prediction of modal mix in road transportation

Transport sector contribution to global emissions is a known fact, however, the mitigation path to achieve nationally determined goals for carbon reduction is often not specified, A simplified technique based on minimax optimization using Grey relational grade and Random forest narrows down on most...

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Autores principales: Rauf, Huma, Umer, Muhammad
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10621837/
https://www.ncbi.nlm.nih.gov/pubmed/37917657
http://dx.doi.org/10.1371/journal.pone.0288493
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author Rauf, Huma
Umer, Muhammad
author_facet Rauf, Huma
Umer, Muhammad
author_sort Rauf, Huma
collection PubMed
description Transport sector contribution to global emissions is a known fact, however, the mitigation path to achieve nationally determined goals for carbon reduction is often not specified, A simplified technique based on minimax optimization using Grey relational grade and Random forest narrows down on most contributing input variables from twelve road transport modes. This is a region-specific, scenario-based technique applied to north Punjab, Province of Pakistan that first categorizes modes based on their emission and then integrates with AI modeling using Deep Neural Network to develop sustainable trade-offs for carbon reduction. The output parameter translates the problem into a systematic iterative technique that predicts optimization options with different scenarios to bring out an environment-friendly transport mix. A 25% reduction applied to the five most emission-releasing modes like Diesel Light and Heavy Duty vehicles, Gas Light and heavy-duty vehicles, and Gas-Cars results in 16.54 MT of Carbon dioxide which is 54.35% reduced to the predicted 36.24 MT for the year 2044. Similarly in another scenario replacing 25% Gas and Diesel Light Duty vehicles respectively by adding 50% Petrol Light Duty vehicles leads to 18.94 MT of emissions which brings the emission value in 2044 at par with emission releases of the year 2014. The technique offers a forward path that allows environment-friendly modal mix combinations based on business-as-usual to offer transport mix solutions for carbon reduction. It is a generalized model that is based on a customized transport mix. Future studies can also be applied to intermodal tradeoffs like rail, air, waterways, etc.
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spelling pubmed-106218372023-11-03 Development of AI-Augmented optimization technique for analysis & prediction of modal mix in road transportation Rauf, Huma Umer, Muhammad PLoS One Research Article Transport sector contribution to global emissions is a known fact, however, the mitigation path to achieve nationally determined goals for carbon reduction is often not specified, A simplified technique based on minimax optimization using Grey relational grade and Random forest narrows down on most contributing input variables from twelve road transport modes. This is a region-specific, scenario-based technique applied to north Punjab, Province of Pakistan that first categorizes modes based on their emission and then integrates with AI modeling using Deep Neural Network to develop sustainable trade-offs for carbon reduction. The output parameter translates the problem into a systematic iterative technique that predicts optimization options with different scenarios to bring out an environment-friendly transport mix. A 25% reduction applied to the five most emission-releasing modes like Diesel Light and Heavy Duty vehicles, Gas Light and heavy-duty vehicles, and Gas-Cars results in 16.54 MT of Carbon dioxide which is 54.35% reduced to the predicted 36.24 MT for the year 2044. Similarly in another scenario replacing 25% Gas and Diesel Light Duty vehicles respectively by adding 50% Petrol Light Duty vehicles leads to 18.94 MT of emissions which brings the emission value in 2044 at par with emission releases of the year 2014. The technique offers a forward path that allows environment-friendly modal mix combinations based on business-as-usual to offer transport mix solutions for carbon reduction. It is a generalized model that is based on a customized transport mix. Future studies can also be applied to intermodal tradeoffs like rail, air, waterways, etc. Public Library of Science 2023-11-02 /pmc/articles/PMC10621837/ /pubmed/37917657 http://dx.doi.org/10.1371/journal.pone.0288493 Text en © 2023 Rauf, Umer https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://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
Rauf, Huma
Umer, Muhammad
Development of AI-Augmented optimization technique for analysis & prediction of modal mix in road transportation
title Development of AI-Augmented optimization technique for analysis & prediction of modal mix in road transportation
title_full Development of AI-Augmented optimization technique for analysis & prediction of modal mix in road transportation
title_fullStr Development of AI-Augmented optimization technique for analysis & prediction of modal mix in road transportation
title_full_unstemmed Development of AI-Augmented optimization technique for analysis & prediction of modal mix in road transportation
title_short Development of AI-Augmented optimization technique for analysis & prediction of modal mix in road transportation
title_sort development of ai-augmented optimization technique for analysis & prediction of modal mix in road transportation
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10621837/
https://www.ncbi.nlm.nih.gov/pubmed/37917657
http://dx.doi.org/10.1371/journal.pone.0288493
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