Data-driven strategies for improving railway ticket demand forecasting accuracy
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Tarih
2024
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
The 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.
Açıklama
IEEE MP Section; Institution of Electronics and Telecommunications Engineers (IETE)
16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024 -- 22 December 2024 through 23 December 2024 -- Indore -- 206392
16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024 -- 22 December 2024 through 23 December 2024 -- Indore -- 206392
Anahtar Kelimeler
ARIMA, CNN, demand forecasting, machine learning, neural networks, OD matrix, origin-destination (OD) pairs, railway system, revenue management, SARIMAX, time series
