{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,27]],"date-time":"2025-12-27T07:30:28Z","timestamp":1766820628633,"version":"build-2065373602"},"reference-count":56,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2024,12,25]],"date-time":"2024-12-25T00:00:00Z","timestamp":1735084800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sustainability"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/su17010041","type":"journal-article","created":{"date-parts":[[2024,12,25]],"date-time":"2024-12-25T19:29:52Z","timestamp":1735154992000},"page":"41","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["The Application of Machine Learning and Deep Learning with a Multi-Criteria Decision Analysis for Pedestrian Modeling: A Systematic Literature Review (1999\u20132023)"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5733-2175","authenticated-orcid":false,"given":"Pedro","family":"Reyes-Norambuena","sequence":"first","affiliation":[{"name":"School of Engineering, Universidad Cat\u00f3lica del Norte, Larrondo 1281, Coquimbo 1781421, Chile"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2953-6688","authenticated-orcid":false,"given":"Alberto Adrego","family":"Pinto","sequence":"additional","affiliation":[{"name":"DM and LIAAD-INESC TEC, Faculty of Sciences, University of Porto, Rua do Campo Alegre, s\/n, 4169-007 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6359-895X","authenticated-orcid":false,"given":"Javier","family":"Mart\u00ednez","sequence":"additional","affiliation":[{"name":"Department of Applied Mathematics I, Telecommunications Engineering School, University of Vigo, 36310 Vigo, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9436-5833","authenticated-orcid":false,"given":"Amir","family":"Karbassi Yazdi","sequence":"additional","affiliation":[{"name":"Departamento de Ingenier\u00eda Industrial y de Sistemas, Facultad de Ingenier\u00eda, Universidad de Tarapac\u00e1, Arica 1000000, Chile"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3482-1574","authenticated-orcid":false,"given":"Yong","family":"Tan","sequence":"additional","affiliation":[{"name":"School of Management, University of Bradford, Bradford BD7 1DP, UK"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3299","DOI":"10.1007\/s10462-020-09948-w","article-title":"Machine Learning towards Intelligent Systems: Applications, Challenges, and Opportunities","volume":"54","author":"Injadat","year":"2021","journal-title":"Artif. Intell. Rev."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Tufail, S., Riggs, H., Tariq, M., and Sarwat, A.I. (2023). Advancements and Challenges in Machine Learning: A Comprehensive Review of Models, Libraries, Applications, and Algorithms. Electronics, 12.","DOI":"10.3390\/electronics12081789"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"58669","DOI":"10.1007\/s11356-024-34862-x","article-title":"Energy Transition in Sustainable Transport: Concepts, Policies, and Methodologies","volume":"31","author":"Collazos","year":"2024","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1767","DOI":"10.1007\/s11116-020-10111-1","article-title":"Design and Analysis of Control Strategies for Pedestrian Flows","volume":"48","author":"Molyneaux","year":"2021","journal-title":"Transportation"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"857","DOI":"10.1080\/1331677X.2016.1237302","article-title":"Hybrid Multiple Criteria Decision-Making Methods: A Review of Applications for Sustainability Issues","volume":"29","author":"Zavadskas","year":"2016","journal-title":"Econ. Res.-Ekon. Istra\u017eivanja"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1007\/s13042-021-01347-z","article-title":"Ensemble of Feature Selection Algorithms: A Multi-Criteria Decision-Making Approach","volume":"13","author":"Hashemi","year":"2022","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Burchart, D., and Przytu\u0142a, I. (2024). Sustainability Assessment Methods for the Transport Sector Considering the Life Cycle Concept\u2014A Review. Sustainability, 16.","DOI":"10.3390\/su16188148"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"102351","DOI":"10.1016\/j.trd.2020.102351","article-title":"Multi-Criteria Analysis of Transport Infrastructure Projects","volume":"83","author":"Broniewicz","year":"2020","journal-title":"Transp. Res. D Transp. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.autcon.2018.11.033","article-title":"Detection of Construction Workers under Varying Poses and Changing Background in Image Sequences via Very Deep Residual Networks","volume":"99","author":"Son","year":"2019","journal-title":"Autom. Constr."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"45","DOI":"10.5194\/ars-10-45-2012","article-title":"Pedestrian Recognition Using Automotive Radar Sensors","volume":"10","author":"Bartsch","year":"2012","journal-title":"Adv. Radio Sci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1709","DOI":"10.1109\/TAES.2018.2799758","article-title":"Deep Convolutional Autoencoder for Radar-Based Classification of Similar Aided and Unaided Human Activities","volume":"54","author":"Seyfioglu","year":"2018","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Everett, M., Chen, Y.F., and How, J.P. (2018, January 1\u20135). Motion Planning among Dynamic, Decision-Making Agents with Deep Reinforcement Learning. Proceedings of the IEEE International Conference on Intelligent Robots and Systems, Madrid, Spain.","DOI":"10.1109\/IROS.2018.8593871"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"732","DOI":"10.1016\/j.trc.2017.09.020","article-title":"Adaptive Traffic Signal Control with Actor-Critic Methods in a Real-World Traffic Network with Different Traffic Disruption Events","volume":"85","author":"Aslani","year":"2017","journal-title":"Transp. Res. Part. C Emerg. Technol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"410","DOI":"10.1016\/j.trf.2021.02.017","article-title":"Evaluating Pedestrian Interaction Preferences with a Game Theoretic Vehicle in Virtual Reality","volume":"78","author":"Camara","year":"2021","journal-title":"Transp. Res. Part F Traffic Psychol. Behav."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1007\/978-3-319-97982-3_16","article-title":"A Study on CNN Transfer Learning for Image Classification","volume":"840","author":"Hussain","year":"2019","journal-title":"Adv. Intell. Syst. Comput."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"909","DOI":"10.1109\/TPAMI.2007.70738","article-title":"Modeling, Clustering, and Segmenting Video with Mixtures of Dynamic","volume":"30","author":"Chan","year":"2008","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_17","first-page":"262","article-title":"Viewpoint Invariant Pedestrian Recognition with an Ensemble of Localized Features","volume":"Volume 5302","author":"Gray","year":"2008","journal-title":"Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1713","DOI":"10.1109\/TPAMI.2008.75","article-title":"Pedestrian Detection via Classification on Riemannian Manifolds","volume":"30","author":"Tuzel","year":"2008","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3243316","article-title":"Unsupervised Person Re-Identification: Clustering and Fine-Tuning","volume":"14","author":"Fan","year":"2018","journal-title":"ACM Trans. Multimed. Comput. Commun. Appl."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.neucom.2018.01.092","article-title":"Computer Vision and Deep Learning Techniques for Pedestrian detection and Tracking: A Survey","volume":"300","author":"Brunetti","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1046","DOI":"10.1109\/JSAC.2017.2680898","article-title":"Caching in the Sky: Proactive Deployment of Cache-Enabled Unmanned Aerial Vehicles for Optimized Quality-of-Experience","volume":"35","author":"Chen","year":"2017","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Schwartz, W.R., Kembhavi, A., Harwood, D., and Davis, L.S. (October, January 29). Human Detection Using Partial Least Squares Analysis. Proceedings of the 2009 IEEE 12th International Conference on Computer Vision, Kyoto, Japan.","DOI":"10.1109\/ICCV.2009.5459205"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"466","DOI":"10.1016\/j.neunet.2018.09.002","article-title":"Soft + Hardwired Attention: An LSTM Framework for Human Trajectory Prediction and Abnormal Event Detection","volume":"108","author":"Fernando","year":"2018","journal-title":"Neural Netw."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.apgeog.2016.09.024","article-title":"Measuring Visual Enclosure for Street Walkability: Using Machine Learning Algorithms and Google Street View Imagery","volume":"76","author":"Yin","year":"2016","journal-title":"Appl. Geogr."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.neucom.2012.01.036","article-title":"Collecting Pedestrian Trajectories","volume":"100","author":"Boltes","year":"2013","journal-title":"Neurocomputing"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Lin, C.-L., Chen, J.K.C., and Ho, H.-H. (2021). BIM for Smart Hospital Management during COVID-19 Using MCDM. Sustainability, 13.","DOI":"10.3390\/su13116181"},{"key":"ref_27","first-page":"361","article-title":"Towards Gender-Inclusive Cities: Prioritizing Safety Parameters for Sustainable Urban Development through Multi-Criteria Decision Analysis","volume":"14","author":"Yadav","year":"2023","journal-title":"Int. J. Sustain. Build. Technol. Urban. Dev."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"105771","DOI":"10.1016\/j.aap.2020.105771","article-title":"A GIS and Microsimulation-Based MCDA Approach for Evaluation of Pedestrian Crossings","volume":"148","author":"Alemdar","year":"2020","journal-title":"Accid. Anal. Prev."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"090009","DOI":"10.1063\/5.0047778","article-title":"MCDA to Evaluate Alternative Paths for Urban Electric Micromobility","volume":"2343","author":"Acampa","year":"2021","journal-title":"AIP Conf. Proc."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1007\/978-3-030-02305-8_38","article-title":"Development of a Methodology, Using Multi-Criteria Decision Analysis (MCDA), to Choose between Full Pedestrianization and Traffic Calming Area (Woonerf Zone Type)","volume":"879","author":"Vasileiadis","year":"2019","journal-title":"Adv. Intell. Syst. Comput."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1057\/s41289-022-00178-w","article-title":"Street Network or Functional Attractors? Capturing Pedestrian Movement Patterns and Urban Form with the Integration of Space Syntax and MCDA","volume":"28","author":"Yang","year":"2023","journal-title":"Urban. Des. Int."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1080\/15568318.2012.713445","article-title":"Non-Motorized Mobility in Central Urban Areas: Application of Multi-Criteria Decision Aid in the City of Campinas, Brazil","volume":"8","author":"Violato","year":"2014","journal-title":"Int. J. Sustain. Transp."},{"key":"ref_33","first-page":"237","article-title":"Spatial Walkability Index (Swi) of Pedestrian Access to Rail Transit Station in Kuala Lumpur City Center","volume":"21","author":"Ruslan","year":"2023","journal-title":"Plan. Malays."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"012023","DOI":"10.1088\/1755-1315\/540\/1\/012023","article-title":"Conceptual Framework for Walkability Assessment for Pedestrian Access to Rail Transit Services by Using Spatial-MCDA","volume":"540","author":"Naharudin","year":"2020","journal-title":"IOP Conf. Ser. Earth Environ. Sci."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"731","DOI":"10.2174\/2213275912666190807113914","article-title":"An Improved Approach to Analyze Accidents and Promote Road Safety Using Association Rule Mining and Multi-Criteria Decision Analysis Methods","volume":"13","author":"Zeinab","year":"2020","journal-title":"Recent Adv. Comput. Sci. Commun."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Eboli, L., Forciniti, C., Mazzulla, G., and Bellizzi, M.G. (2023). Establishing Performance Criteria for Evaluating Pedestrian Environments. Sustainability, 15.","DOI":"10.3390\/su15043523"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Manzolli, J.A., Oliveira, A., and Neto, M.D.C. (2021). Evaluating Walkability through a Multi-Criteria Decision Analysis Approach: A Lisbon Case Study. Sustainability, 13.","DOI":"10.3390\/su13031450"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"100923","DOI":"10.1016\/j.seps.2020.100923","article-title":"Mapping Walkability. A Subjective Value Theory Approach","volume":"72","author":"Fancello","year":"2020","journal-title":"Socioecon. Plann Sci."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Ahmed, S.M., Rao, P.R., Chakrabarty, N., and Pothula, V.K. (2023). Counter Measures to Control and Reduce Fatal Accidents by Improving Driving Capabilities in Aged Adults in India. Cognitive Science and Technology, Springer.","DOI":"10.1007\/978-981-19-2358-6_63"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Sharma, R., Mahanti, G.K., Panda, G., Rath, A., Dash, S., Mallik, S., and Hu, R. (2023). A Framework for Detecting Thyroid Cancer from Ultrasound and Histopathological Images Using Deep Learning, Meta-Heuristics, and MCDM Algorithms. J. Imaging, 9.","DOI":"10.3390\/jimaging9090173"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2634","DOI":"10.3906\/elk-2103-65","article-title":"Benchmarking of Deep Learning Algorithms for Skin Cancer Detection Based on a Hybrid Framework of Entropy and VIKOR Techniques","volume":"29","author":"Yas","year":"2021","journal-title":"Turk. J. Electr. Eng. Comput. Sci."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"106662","DOI":"10.1016\/j.engappai.2023.106662","article-title":"Multiple-Criteria Decision Making, Feature Selection, and Deep Learning: A Golden Triangle for Heart Disease Identification","volume":"125","author":"Najafi","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"102390","DOI":"10.1016\/j.artmed.2022.102390","article-title":"Mathematical Modeling and AI Based Decision Making for COVID-19 Suspects Backed by Novel Distance and Similarity Measures on Plithogenic Hypersoft Sets","volume":"132","author":"Ahmad","year":"2022","journal-title":"Artif. Intell. Med."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"110035","DOI":"10.1016\/j.asoc.2023.110035","article-title":"One-Dimensional VGGNet for High-Dimensional Data","volume":"135","author":"Feng","year":"2023","journal-title":"Appl. Soft Comput."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Mohamed, H., Al-Masri, E., Kotevska, O., and Souri, A. (2022). A Multi-Objective Approach for Optimizing Edge-Based Resource Allocation Using TOPSIS. Electronics, 11.","DOI":"10.3390\/electronics11182888"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"115215","DOI":"10.1016\/j.scriptamat.2022.115215","article-title":"High-Throughput Investigation of Nb and Ta Alloying Effects on the Microstructure and Properties of a Novel Ni-Co-Based Superalloy","volume":"226","author":"Zhu","year":"2023","journal-title":"Scr. Mater."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"118838","DOI":"10.1016\/j.jenvman.2023.118838","article-title":"Novel Integrated Modelling Based on Multiplicative Long Short-Term Memory (MLSTM) Deep Learning Model and Ensemble Multi-Criteria Decision Making (MCDM) Models for Mapping Flood Risk","volume":"345","author":"Mohammadifar","year":"2023","journal-title":"J. Environ. Manag."},{"key":"ref_48","first-page":"959","article-title":"Bibliometrix: An R-Tool for Comprehensive Science Mapping Analysis","volume":"11","author":"Aria","year":"2017","journal-title":"J. Inf."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"n160","DOI":"10.1136\/bmj.n160","article-title":"PRISMA 2020 Explanation and Elaboration: Updated Guidance and Exemplars for Reporting Systematic Reviews","volume":"372","author":"Page","year":"2021","journal-title":"BMJ"},{"key":"ref_50","first-page":"VII","article-title":"Will You Take This Turn? Gaze-Based Turning Activity Recognition during Navigation","volume":"Volume 208","author":"Janowicz","year":"2021","journal-title":"Proceedings of the Leibniz International Proceedings in Informatics, 11th International Conference on Geographic Information Science (GIScience 2021)\u2014Part II"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1007\/978-981-15-4029-5_5","article-title":"Lane Detection and Collision Prevention System for Automated Vehicles","volume":"1155","author":"Agrawal","year":"2020","journal-title":"Adv. Intell. Syst. Comput."},{"key":"ref_52","first-page":"688","article-title":"Graph Representation Method for Pedestrian Intention Recognition of Intelligent Vehicle","volume":"42","author":"Chao","year":"2022","journal-title":"Beijing Ligong Daxue Xuebao\/Trans. Beijing Inst. Technol."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"88308","DOI":"10.1109\/ACCESS.2020.2993767","article-title":"A Deep Learning Framework for Detection of Targets in Thermal Images to Improve Firefighting","volume":"8","author":"Bhattarai","year":"2020","journal-title":"IEEE Access"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"104760","DOI":"10.1016\/j.ssci.2020.104760","article-title":"Empirical Methods in Pedestrian, Crowd and Evacuation Dynamics: Part II. Field Methods and Controversial Topics","volume":"129","author":"Haghani","year":"2020","journal-title":"Saf. Sci."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1016\/j.aap.2016.06.009","article-title":"Red-Light Running Violation Prediction Using Observational and Simulator Data","volume":"96","author":"Jahangiri","year":"2016","journal-title":"Accid. Anal. Prev."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"5453","DOI":"10.1109\/TITS.2020.3006767","article-title":"Pedestrian Models for Autonomous Driving Part II: High-Level Models of Human Behavior","volume":"22","author":"Camara","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."}],"container-title":["Sustainability"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2071-1050\/17\/1\/41\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:59:54Z","timestamp":1760115594000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2071-1050\/17\/1\/41"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,25]]},"references-count":56,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,1]]}},"alternative-id":["su17010041"],"URL":"https:\/\/doi.org\/10.3390\/su17010041","relation":{},"ISSN":["2071-1050"],"issn-type":[{"type":"electronic","value":"2071-1050"}],"subject":[],"published":{"date-parts":[[2024,12,25]]}}}