Simulation, Optimization, and Machine Learning in Sustainable Transportation Systems: Models and Applications

Rocio de la Torre, Canan G. Corlu, Javier Faulin, Bhakti S. Onggo and Angel A. Juan
Additional contact information
Rocio de la Torre: INARBE Institute, Department of Business Management, Public University of Navarre, 31006 Pamplona, Spain
Canan G. Corlu: Metropolitan College, Boston University, Boston, MA 02215, USA
Javier Faulin: Institute of Smart Cities, Department of Statistics, Computer Science, and Mathematics, Public University of Navarre, 31006 Pamplona, Spain
Bhakti S. Onggo: Centre for Operational Research Management Sciences and Information Systems (CORMSIS), University of Southampton, Southampton SO17 1BJ, UK
Angel A. Juan: IN3—Computer Science Department, Universitat Oberta de Catalunya & Euncet Business School, 08018 Barcelona, Spain

Sustainability, 2021, vol. 13, issue 3, 1-21

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.

Keywords: transportation systems; sustainability; simulation; optimization; machine learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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