Which Distance-Decay Function Can Improve the Goodness of Fit of the Metro Station Ridership Regression Model? A Case Study of Beijing
Incorporating the distance-decay effects of facility points into the analysis of metro ridership helps generate more precise and actionable strategies for station area renewal. The majority of existing studies, however, calculated the built environment variables based on the same pedestrian catchmen...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
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| Series: | Buildings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-5309/15/10/1686 |
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| Summary: | Incorporating the distance-decay effects of facility points into the analysis of metro ridership helps generate more precise and actionable strategies for station area renewal. The majority of existing studies, however, calculated the built environment variables based on the same pedestrian catchment areas (PCAs) of metro stations and failed to consider the impact of distance decay from POIs (points of interest) on the accuracy of metro station ridership regression models. The objective of this study is to determine which distance-decay function best improves the fit of the metro ridership regression model and investigate the effect of the built environment on ridership under the optimal distance-decay model. Based on the distribution density of metro stations in Beijing, the research area is divided into three zones with different PCAs. Built environment variables for all metro stations are aggregated according to the PCA scope. Various distance-decay functions are examined to determine how the accuracy of the Multi-scale Geographically Weighted Regression (MGWR) model is affected by built environment variables calculated from POI facilities (Gaussian distance decay, power distance decay, piecewise distance decay). Finally, under optimal distance decay, the MGWR model is used to investigate how the built environment influences metro ridership. The results show the following: (1) The Gaussian distance-decay function improves the goodness of fit of the regression model, resulting in an 11.25% increase in the <i>R</i><sup>2</sup> value when compared to the model without a distance-decay function. (2) During morning peak hours, apartment and office density significantly impacts ridership. The proposed research framework is conducive to improving the accuracy of the metro station ridership regression model. Moreover, it facilitates the formulation of targeted strategies for the renewal of the built environment by government managers and planners. |
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| ISSN: | 2075-5309 |