Influential Factor Analysis and Prediction on Initial Metro Network Ridership in Xi’an, China
To satisfy the adaptability of forecasting the short-term and abrupt volume of the initial metro network, the authors build the multiple enter linear regression (MELR) model to explore the determinants and forecast the intensity during the twice expansion of the initial metro network in Xi’an. The authors further compare the prediction of the metro transport capacity between the MELR models with exponential smoothing and autoregressive integrated moving average (ARIMA) models. Results show that the passenger intensity significantly fluctuates with the months and days, and MELR model is more adapted for the short-term prediction of the abrupt volume than the ARIMA model during the new metro line opening and the old line expands, which avoids the drawback of time series models that need a huge database. This study provides a guide for the prediction of initial metro network volume and accurate purchase of the rail vehicles during the metro planning and expends stages.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/oclc/5121625
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Supplemental Notes:
- © 2022 Tao Lyu et al.
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Authors:
- Lyu, Tao
- Xu, Mingfei
- Zhang, Jia
- Wang, Yuanqing
- Yang, Liu
- Gao, Yanan
- Publication Date: 2022-5
Language
- English
Media Info
- Media Type: Web
- Features: Figures; References; Tables;
- Pagination: Article ID 2842949
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Serial:
- Journal of Advanced Transportation
- Volume: 2022
- Publisher: John Wiley & Sons, Incorporated
- ISSN: 0197-6729
- EISSN: 2042-3195
- Serial URL: http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)2042-3195
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Publication flags:
Open Access (libre)
Subject/Index Terms
- TRT Terms: Factor analysis; Linear regression analysis; Mathematical prediction; Ridership; Subways
- Identifier Terms: Autoregressive Integrated Moving Average (ARIMA)
- Geographic Terms: Xi'an (China)
- Subject Areas: Passenger Transportation; Public Transportation;
Filing Info
- Accession Number: 01846877
- Record Type: Publication
- Files: TRIS
- Created Date: May 25 2022 9:35AM