Understanding walking behaviour during COVID-19: Binary Logit and ANN approach

Basit Öğe Kaydı
dc.authorid0000-0003-4433-1998
dc.authorid0000-0003-0597-1511
dc.contributor.authorGunay, Gurkan
dc.contributor.authorDundar, Selim
dc.date.accessioned2026-04-04T18:56:04Z
dc.date.available2026-04-04T18:56:04Z
dc.date.issued2024
dc.departmentİstanbul Bilgi Üniversitesi
dc.description.abstractThe COVID-19 pandemic had severe impacts on society. It negatively affected many sectors, and transportation is one of those. Naturally, the walking trip behavior of individuals was also altered. This study aims to investigate the changes in walking trips of individuals with two models: Binary Logit (BL) and Artificial Neural Networks (ANN). An online survey was conducted with 387 individuals. BL model investigated if respondents' walking trips would increase during and after the pandemic. On the other hand, ANN models were developed to determine the significant factors in changes in walking behavior. Results indicate that ANN models capture more factors than BL models. Demographics and attitudes towards public transportation, taxi, and walking trips during the pandemic are found to be effective in walking behavior changes. Policies can be made to increase public transit ridership, and infrastructure for walking can be improved. Future research suggestions are given.
dc.description.sponsorshipEuropean Union's H2020 research and innovation program under the RECIPROCITY Project [101006576]
dc.description.sponsorshipThis study has been prepared with the support of the European Union's H2020 research and innovation program under the RECIPROCITY Project (Grant NO 101006576).
dc.identifier.doi10.1680/jmuen.23.00039
dc.identifier.doi10.1680/jmuen.23.00039
dc.identifier.issn0965-0903
dc.identifier.issn1751-7699
dc.identifier.scopus2-s2.0-85209636174
dc.identifier.scopusqualityQ3
dc.identifier.urihttps://doi.org/10.1680/jmuen.23.00039
dc.identifier.urihttps://hdl.handle.net/11411/10661
dc.identifier.wosWOS:001354472000001
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherEmerald Group Publishing Ltd
dc.relation.ispartofProceedings of the Institution of Civil Engineers-Municipal Engineer
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WoS_20260402
dc.snmzKA_Scopus_20260402
dc.subjectCovid-19
dc.subjectNeural Networks
dc.subjectBinary Logit
dc.subjectTransport Planning
dc.subjectTransport Management
dc.titleUnderstanding walking behaviour during COVID-19: Binary Logit and ANN approach
dc.typeArticle

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