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Research on Quality Management of Urban Rail Transit Vehicle Frame Overhaul Project Read chapter Read first chapter

2023 | OriginalPaper | Chapter

2. Modeling and Analysis of Railway Passenger Flow Forecast During the Spring Festival

Authors : Zhi-Cheng Zhang, Ding Chen, Pei-Zhou Jiang

Published in: Advances in Smart Vehicular Technology, Transportation, Communication and Applications

Publisher: Springer Nature Singapore

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Abstract

The sharp increase in railway passenger flow during the Spring Festival Travel Season has tested the organization and dispatching ability of the railway transportation system. In this paper, the advantages of least square support vector machine (LSSVM) in small sample data prediction are integrated, and the ARIMA-LSSVM hybrid model based on residual linear transfer superposition is proposed, which is verified by Xiamen Spring Festival railway passenger flow. The analysis results show that the average absolute errors of hybrid model are 0.565 × 104 and 0.979 × 104 person times, respectively, which are 22.50% and 12.43% higher than ARIMA model, and 28.30% and 18.35% higher than LSSVM model. This study plays a positive role in improving the railway passenger flow forecasting ability and adjusting the preparation time during the Spring Festival Travel Season.

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previous chapter Research on Quality Management of Urban Rail Transit Vehicle Frame Overhaul Project
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Metadata
Title
Modeling and Analysis of Railway Passenger Flow Forecast During the Spring Festival
Authors
Zhi-Cheng Zhang
Ding Chen
Pei-Zhou Jiang
Copyright Year
2023
Publisher
Springer Nature Singapore
Book
Advances in Smart Vehicular Technology, Transportation, Communication and Applications

Print ISBN: 978-981-9908-47-9

Electronic ISBN: 978-981-9908-48-6

Copyright Year: 2023

DOI
https://doi.org/10.1007/978-981-99-0848-6_2

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