The Application of Machine Learning and Deep Learning with a Multi-Criteria Decision Analysis for Pedestrian Modeling: A Systematic Literature Review (1999–2023)

Pedro Reyes-Norambuena, Alberto Adrego Pinto, Javier Martínez, Amir Karbassi Yazdi () and Yong Tan
Additional contact information
Pedro Reyes-Norambuena: School of Engineering, Universidad Católica del Norte, Larrondo 1281, Coquimbo 1781421, Chile
Alberto Adrego Pinto: DM and LIAAD-INESC TEC, Faculty of Sciences, University of Porto, Rua do Campo Alegre, s/n, 4169-007 Porto, Portugal
Javier Martínez: Department of Applied Mathematics I, Telecommunications Engineering School, University of Vigo, 36310 Vigo, Spain
Amir Karbassi Yazdi: Departamento de Ingeniería Industrial y de Sistemas, Facultad de Ingeniería, Universidad de Tarapacá, Arica 1000000, Chile
Yong Tan: School of Management, University of Bradford, Bradford BD7 1DP, UK

Sustainability, 2024, vol. 17, issue 1, 1-27

Abstract: Among transportation researchers, pedestrian issues are highly significant, and various solutions have been proposed to address these challenges. These approaches include Multi-Criteria Decision Analysis (MCDA) and machine learning (ML) techniques, often categorized into two primary types. While previous studies have addressed diverse methods and transportation issues, this research integrates pedestrian modeling with MCDA and ML approaches. This paper examines how MCDA and ML can be combined to enhance decision-making in pedestrian dynamics. Drawing on a review of 1574 papers published from 1999 to 2023, this study identifies prevalent themes and methodologies in MCDA, ML, and pedestrian modeling. The MCDA methods are categorized into weighting and ranking techniques, with an emphasis on their application to complex transportation challenges involving both qualitative and quantitative criteria. The findings suggest that hybrid MCDA algorithms can effectively evaluate ML performance, addressing the limitations of traditional methods. By synthesizing the insights from the existing literature, this review outlines key methodologies and provides a roadmap for future research in integrating MCDA and ML in pedestrian dynamics. This research aims to deepen the understanding of how informed decision-making can enhance urban environments and improve pedestrian safety.

Keywords: machine learning; multi-criteria decision analysis; pedestrian modeling; quantitative statistics methods; research content (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/17/1/41/pdf (application/pdf)
https://www.mdpi.com/2071-1050/17/1/41/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:17:y:2024:i:1:p:41-:d:1552911

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().