Predicting the Choice of Online or Offline Shopping Trips Using a Deep Neural Network Model and Time Series Data: A Case Study of Tehran, Iran

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dc.authoridAllahviranloo, Tofigh/0000-0002-6673-3560
dc.authoridDasoomi, MohammadHanif/0009-0008-2433-3578
dc.authoridNaderan, Ali/0000-0001-5842-0188
dc.authorwosidAllahviranloo, Tofigh/V-4843-2019
dc.contributor.authorDasoomi, Mohammadhanif
dc.contributor.authorNaderan, Ali
dc.contributor.authorAllahviranloo, Tofigh
dc.date.accessioned2024-05-19T14:46:06Z
dc.date.available2024-05-19T14:46:06Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.description.abstractThis study examines the determinants of online and offline shopping trip choices and their implications for urban transportation, the environment, and the economy in Tehran, Iran. A questionnaire survey was conducted to collect data from 1000 active e-commerce users who successfully placed orders through both online and offline services in districts 2 and 5 of Tehran during the last 20 days of 2021. A deep neural network model was applied to predict the type of shopping trips based on 10 variables including age, gender, car ownership, delivery cost, and product price. The model's performance was evaluated against four other algorithms: MLP, decision tree, LSTM, and KNN. The results demonstrated that the deep neural network model achieved the highest accuracy, with a rate of 95.73%. The most important factors affecting the choice of shopping trips were delivery cost, delivery time, and product price. This study offers valuable insights for transportation planners, e-commerce managers, and policymakers. It aims to help them design effective strategies to reduce transportation costs, lower pollutant emissions, alleviate urban traffic congestion, and enhance user satisfaction all while promoting sustainable development.en_US
dc.identifier.doi10.3390/su152014764
dc.identifier.issn2071-1050
dc.identifier.issue20en_US
dc.identifier.urihttps://doi.org10.3390/su152014764
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5443
dc.identifier.volume15en_US
dc.identifier.wosWOS:001095190100001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherMdpien_US
dc.relation.ispartofSustainabilityen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz20240519_kaen_US
dc.subjectOnline Shopping Tripen_US
dc.subjectOffline Shopping Tripsen_US
dc.subjectDeep Neural Network Modelen_US
dc.subjectE-Commerce And Transportationen_US
dc.subjectFactors Affecting Shopping Trip Choiceen_US
dc.subjectSustainable Developmenten_US
dc.titlePredicting the Choice of Online or Offline Shopping Trips Using a Deep Neural Network Model and Time Series Data: A Case Study of Tehran, Iranen_US
dc.typeArticleen_US

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