Asutay, AslıUğurlu, Onur2025-03-202025-03-202024978-303152759-32522-8595https://doi.org/10.1007/978-3-031-52760-9_3https://hdl.handle.net/20.500.14034/20452nd International Congress of Electrical and Computer Engineering, ICECENG 2023 -- 22 November 2023 through 25 November 2023 -- Bandirma -- 309799Public 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.eninfo:eu-repo/semantics/closedAccessDeep learning algorithmsMachine learningPassenger number forecastingPublic transportationRail systemConference Object10.1007/978-3-031-52760-9_331432-s2.0-85189512266Q3