Research on Demand Forecast of Key Components for Railway Freight Cars Based on LSTM Recurrent Neural Network

Dan Chang (), Linhao Sun () and Gengrun Li ()
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Dan Chang: Beijing Jiaotong University
Linhao Sun: Beijing Jiaotong University
Gengrun Li: Beijing Jiaotong University

A chapter in LISS 2024, 2025, pp 1232-1240 from Springer

Abstract: Abstract Currently, the railway freight car industry in China is experiencing rapid development. However, due to the disconnection between the maintenance production plan and the production material demand, railway equipment companies often encounter issues such as significant waste of material components and insufficient inventory of raw materials for key parts during the process of vehicle inspection, maintenance, and operation. Leveraging big data for scientific component demand forecasting is a necessary approach to achieving refined management of railway equipment maintenance. This study utilizes the material consumption data of GN Railway Equipment Company and employs NLP text analysis to identify the key components for railway freight car maintenance. Then, a demand forecasting model is constructed based on LSTM. Finally, the LSTM model is compared with other forecasting models, including CNN model prediction and linear regression prediction, to verify its capability in component forecasting. This research utilizes the LSTM long short-term memory neural network to build a demand forecasting model for predicting the demand of key components of railway freight cars, thus providing informationalized and accurate guidance for railway freight car maintenance.

Keywords: Railway wagons; Key accessories; LSTM; Demand forecasting (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnopch:978-981-96-9697-0_94

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DOI: 10.1007/978-981-96-9697-0_94

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