Prediction of Urban Rail Transit Ridership under Rainfall Weather Conditions
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 146, Issue 7
Abstract
Existing studies show that rainfall has a significant impact on bus ridership, and few studies exist on the impact of rainfall on urban rail transit (URT) ridership. Based on the daily and hourly URT ridership and rainfall data collected in Guangzhou, China during continuous thirteen months, this study explores the effects of rainfall on URT ridership and proposes a prediction approach of URT ridership under rainfall conditions, which is calculated by the sum of background ridership and rainfall influenced ridership. First, the Seasonal Autoregressive Integrated Moving Average model is employed to predict background ridership. Next, the rainfall impact factor is proposed and estimated using the Support Vector Regression model. Finally, the last month of data are applied to validate the performance of the proposed approach. The results show that the proposed approach performs well in both daily and hourly URT ridership prediction and, thus, provides a novel solution for quantifying the impact of rainfall on URT ridership, enabling URT managers to better understand the ridership variations under rainfall conditions and react well to it.
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Data Availability Statement
Some or all of the data, models, or code generated or used during this study are proprietary or confidential and may only be provided with restrictions. The ridership data of Guangzhou Metro used for the case study are confidential and can only be provided after anonymization.
Acknowledgments
This work was supported by the Fundamental Research Funds for the Central Universities (No. 2019YJS108).
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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 146 • Issue 7 • July 2020
Copyright
©2020 American Society of Civil Engineers.
History
Received: Aug 1, 2019
Accepted: Jan 31, 2020
Published online: May 8, 2020
Published in print: Jul 1, 2020
Discussion open until: Oct 8, 2020
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