Data-driven strategies for improving railway ticket demand forecasting accuracy

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dc.contributor.authorBoltaikhanova, Tomiris
dc.contributor.authorDael, Fares Abdulhafidh Derhem
dc.contributor.authorShayea, Ibraheem
dc.contributor.authorLeila, Rzayeva
dc.date.accessioned2025-03-20T09:44:58Z
dc.date.available2025-03-20T09:44:58Z
dc.date.issued2024
dc.departmentİzmir Bakırçay Üniversitesi
dc.descriptionIEEE MP Section; Institution of Electronics and Telecommunications Engineers (IETE)
dc.description16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024 -- 22 December 2024 through 23 December 2024 -- Indore -- 206392
dc.description.abstractThe accurate prediction of railway ticket demand is vital for effective operational planning and resource management in the transportation sector. This study investigates various time series analysis techniques, including ARIMA, SARIMAX, and neural networks such as Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN), to forecast railway ticket demand. Utilizing an extensive dataset of ticket sales spanning several years, we trained and validated these models, evaluating their performance through key metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). Demand patterns were represented using Origin-Destination (OD) matrices, where the CNN model was employed to predict the entire OD matrix, while the other models focused on individual OD pairs. The findings reveal that the CNN model outperforms ARIMA, SARIMAX, and LSTM in terms of prediction accuracy, offering a more reliable approach for forecasting demand in railway networks. This study underscores the importance of data-driven strategies in enhancing the precision of demand forecasting, thereby contributing to more informed decision-making and optimized railway operations. © 2024 IEEE.
dc.identifier.doi10.1109/CICN63059.2024.10847408
dc.identifier.endpage1398
dc.identifier.isbn979-833150526-4
dc.identifier.scopus2-s2.0-85218060972
dc.identifier.scopusqualityN/A
dc.identifier.startpage1391
dc.identifier.urihttps://doi.org/10.1109/CICN63059.2024.10847408
dc.identifier.urihttps://hdl.handle.net/20.500.14034/2098
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartofProceedings - 2024 IEEE 16th International Conference on Communication Systems and Network Technologies, CICN 2024
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_Scopus_20250319
dc.subjectARIMA
dc.subjectCNN
dc.subjectdemand forecasting
dc.subjectmachine learning
dc.subjectneural networks
dc.subjectOD matrix
dc.subjectorigin-destination (OD) pairs
dc.subjectrailway system
dc.subjectrevenue management
dc.subjectSARIMAX
dc.subjecttime series
dc.titleData-driven strategies for improving railway ticket demand forecasting accuracy
dc.typeConference Object

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