Ridership Prediction of Urban Rail Transit Stations Based on AFC and POI Data
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 149, Issue 9
Abstract
Ridership prediction of urban rail transit stations is of great significance for the operation and management of rail transit and configuration of facilities around stations. This study used automatic fare collection (AFC) data of the rail transit in Nanjing, China, for a month to obtain station ridership. Based on the point of interest (POI) data (within 800 m around urban rail transit stations), built environment factors such as land type and station accessibility were extracted, and a variable set of built environment factors was then established. Multiple collinearity and spatial autocorrelation analyses were used to screen the variables used in the regression model. A geographically weighted regression (GWR) model was constructed to explore the spatial heterogeneity of the influence on ridership of the built environment around the urban rail stations and to predict ridership. The results show that the GWR model can effectively capture the spatial heterogeneity of the effect of built environment factors on station ridership, and its ridership prediction accuracy is significantly better than that of the ordinary least squares model.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This research was funded by the Scientific Research Foundation for Advanced Talents of Nanjing Forestry University (Grant No. 163106041), the General Project of Philosophy and Social Science Foundation of the Jiangsu Higher Education Institutions of China (Grant No. 2020SJA0125), and the General Program of Natural Science Foundation of the Jiangsu Higher Education Institutions of China (Grant No. 20KJB580013).
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Information & Authors
Information
Published In
Journal of Transportation Engineering, Part A: Systems
Volume 149 • Issue 9 • September 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Nov 10, 2022
Accepted: Feb 7, 2023
Published online: Jun 17, 2023
Published in print: Sep 1, 2023
Discussion open until: Nov 17, 2023
ASCE Technical Topics:
- Analysis (by type)
- Buildings
- Engineering fundamentals
- Infrastructure
- Model accuracy
- Models (by type)
- Public transportation
- Rail transportation
- Railroad stations
- Regression analysis
- Ridership
- Statistical analysis (by type)
- Structural engineering
- Structures (by type)
- Terminal facilities
- Transportation engineering
- Transportation management
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