Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Prediction of Urban Rail Transit Ridership under Rainfall Weather Conditions
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.
Prediction of Urban Rail Transit Ridership under Rainfall Weather Conditions
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.
Prediction of Urban Rail Transit Ridership under Rainfall Weather Conditions
Xue, Fei (Autor:in) / Yao, Enjian (Autor:in) / Huan, Ning (Autor:in) / Li, Binbin (Autor:in) / Liu, Shasha (Autor:in)
08.05.2020
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
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