What question did this study set out to answer?

This research aims to identify the various factors influencing metro ridership on extreme demand days in Guangzhou.

May 9, 2026Open Access

Multidimensional station level determinants of metro ridership during extreme demand days in Guangzhou

Key Points

This research aims to identify the various factors influencing metro ridership on extreme demand days in Guangzhou.
Analyzed ridership data from 100 metro stations in Guangzhou on peak days (December 31, 2024, and January 1, 2025).
Utilized a multidimensional framework considering built environment, intermodal interaction, network topology, and operational features.
Tested three models (OLS, XGBoost, LightGBM) for analysis, selecting LightGBM for further interpretation using SHAP.
Operational features, especially the capacity coefficient, were found to significantly influence ridership, exceeding traditional predictors.
Commercial area, parking lots, and betweenness centrality were also identified as important factors affecting ridership.
The study established a novel application of Line 11 as a spatial boundary for defining the downtown area.

Abstract

Understanding metro ridership drivers is vital for transit planning. This study employs a multidimensional framework–encompassing the built environment, intermodal interaction, network topology, and operational features–to analyze ridership at 100 Guangzhou metro stations, focusing on two peak days: 31 December 2024 (12.2 million riders) and 1 January 2025 (national record). Three models (OLS, XGBoost, LightGBM) are tested, with LightGBM selected. SHAP is used to interpret the model and identify key variables. Results show that operational features, especially the capacity coefficient (CO), exert the greatest influence, surpassing traditional factors. Other significant predictors include commercial area (CA), parking lots (PL), and betweenness centrality (BC). The study also introduces a novel application of Line 11 as a spatial boundary for defining the downtown area. These findings provide actionable insights for enhancing metro operations, capacity, and multimodal integration, demonstrating the utility of machine learning and SHAP-based interpretation in understanding metro ridership under extreme conditions.

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Cite This Study

Bingjie Zhan (Thu,) studied this question.

synapsesocial.com/papers/69fecfafb9154b0b82876afe https://doi.org/https://doi.org/10.1007/s44327-026-00239-7

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