Forecasting the Number of Passengers in Rail System by Deep Learning Algorithms

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dc.contributor.authorAsutay, Aslı
dc.contributor.authorUğurlu, Onur
dc.date.accessioned2025-03-20T09:44:54Z
dc.date.available2025-03-20T09:44:54Z
dc.date.issued2024
dc.departmentİzmir Bakırçay Üniversitesi
dc.description2nd International Congress of Electrical and Computer Engineering, ICECENG 2023 -- 22 November 2023 through 25 November 2023 -- Bandirma -- 309799
dc.description.abstractPublic transportation has emerged as an important factor in selecting and developing urban centers, including diverse sectors, such as commerce, social engagement, education, and healthcare. The relationship between transportation and urban development underlines the crucial role of transportation in cities. The continuous increase in population density has led to a rapid increase in the number of urban passengers, thereby intensifying the complexity of public transportation networks. Consequently, developing strategic short-term and long-term transportation plans has become an essential task. In this study, we developed a prediction model for estimating the number of passengers in subways. In this manner, we used Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) algorithms, which are reported to have high predictive performance in time series. To test the developed model, we used New York Subway data. The results show that the RNN algorithm has a high prediction performance in estimating the number of passengers. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
dc.identifier.doi10.1007/978-3-031-52760-9_3
dc.identifier.endpage43
dc.identifier.isbn978-303152759-3
dc.identifier.issn2522-8595
dc.identifier.scopus2-s2.0-85189512266
dc.identifier.scopusqualityQ3
dc.identifier.startpage31
dc.identifier.urihttps://doi.org/10.1007/978-3-031-52760-9_3
dc.identifier.urihttps://hdl.handle.net/20.500.14034/2045
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer Science and Business Media Deutschland GmbH
dc.relation.ispartofEAI/Springer Innovations in Communication and Computing
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_Scopus_20250319
dc.subjectDeep learning algorithms
dc.subjectMachine learning
dc.subjectPassenger number forecasting
dc.subjectPublic transportation
dc.subjectRail system
dc.titleForecasting the Number of Passengers in Rail System by Deep Learning Algorithms
dc.typeConference Object

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