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Simulation, optimization, and machine learning in sustainable transportation systems: models and applications

Simulation, optimization, and machine learning in sustainable transportation systems: models and applications
Simulation, optimization, and machine learning in sustainable transportation systems: models and applications
The need for effective freight and human transportation systems has consistently increased during the last decades, mainly due to factors such as globalization, e-commerce activities, and mobility requirements. Traditionally, transportation systems have been designed with the main goal of reducing their monetary cost while offering a specified quality of service. During the last decade, however, sustainability concepts are also being considered as a critical component of transportation systems, i.e., the environmental and social impact of transportation activities have to be taken into account when managers and policy makers design and operate modern transportation systems, whether these refer to long-distance carriers or to metropolitan areas. This paper reviews the existing work on different scientific methodologies that are being used to promote Sustainable Transportation Systems (STS), including simulation, optimization, machine learning, and fuzzy sets. This paper discusses how each of these methodologies have been employed to design and efficiently operate STS. In addition, the paper also provides a classification of common challenges, best practices, future trends, and open research lines that might be useful for both researchers and practitioners
Machine learning, Optimization, Simulation, Sustainability, Transportation systems
2071-1050
de la Torre, Rocio
414ea334-70cd-4299-b4c8-836a3fc77439
Corlu, Canan Gunes
ecb0f999-21d4-41e2-8cab-58a33706f09e
Faulin, Javier
b50f3d35-0d75-4c02-be1c-57bb671fa5ae
Onggo, Bhakti Stephan
8e9a2ea5-140a-44c0-9c17-e9cf93662f80
Juan, Angel A.
a08d6aac-1e9b-4537-81a7-29a1ba791f26
de la Torre, Rocio
414ea334-70cd-4299-b4c8-836a3fc77439
Corlu, Canan Gunes
ecb0f999-21d4-41e2-8cab-58a33706f09e
Faulin, Javier
b50f3d35-0d75-4c02-be1c-57bb671fa5ae
Onggo, Bhakti Stephan
8e9a2ea5-140a-44c0-9c17-e9cf93662f80
Juan, Angel A.
a08d6aac-1e9b-4537-81a7-29a1ba791f26

de la Torre, Rocio, Corlu, Canan Gunes, Faulin, Javier, Onggo, Bhakti Stephan and Juan, Angel A. (2021) Simulation, optimization, and machine learning in sustainable transportation systems: models and applications. Sustainability, 13 (3), [1551]. (doi:10.3390/su13031551).

Record type: Article

Abstract

The need for effective freight and human transportation systems has consistently increased during the last decades, mainly due to factors such as globalization, e-commerce activities, and mobility requirements. Traditionally, transportation systems have been designed with the main goal of reducing their monetary cost while offering a specified quality of service. During the last decade, however, sustainability concepts are also being considered as a critical component of transportation systems, i.e., the environmental and social impact of transportation activities have to be taken into account when managers and policy makers design and operate modern transportation systems, whether these refer to long-distance carriers or to metropolitan areas. This paper reviews the existing work on different scientific methodologies that are being used to promote Sustainable Transportation Systems (STS), including simulation, optimization, machine learning, and fuzzy sets. This paper discusses how each of these methodologies have been employed to design and efficiently operate STS. In addition, the paper also provides a classification of common challenges, best practices, future trends, and open research lines that might be useful for both researchers and practitioners

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2020_de_la_Torre_Corlu_Onggo___Sustainable_Transport_Systems (1) - Accepted Manuscript
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Accepted/In Press date: 26 January 2021
Published date: 2 February 2021
Keywords: Machine learning, Optimization, Simulation, Sustainability, Transportation systems

Identifiers

Local EPrints ID: 446944
URI: http://eprints.soton.ac.uk/id/eprint/446944
ISSN: 2071-1050
PURE UUID: 1de6efbe-19cb-4ae3-9f9f-db20c28483ef
ORCID for Bhakti Stephan Onggo: ORCID iD orcid.org/0000-0001-5899-304X

Catalogue record

Date deposited: 26 Feb 2021 17:33
Last modified: 27 Apr 2022 02:13

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Contributors

Author: Rocio de la Torre
Author: Canan Gunes Corlu
Author: Javier Faulin
Author: Angel A. Juan

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