Nonlinear and Threshold Effects on Station-Level Ridership: Insights from Disproportionate Weekday-to-Weekend Impacts
Station-level ridership is an important indicator for understanding the relationship between land use and rail transit, which is crucial for building more sustainable urban mobility systems. However, the nonlinear effects of the built environment on metro ridership, particularly concerning temporal...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-10-01
|
| Series: | ISPRS International Journal of Geo-Information |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2220-9964/13/10/365 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850205587599523840 |
|---|---|
| author | Yanyan Gu Mingxuan Dou |
| author_facet | Yanyan Gu Mingxuan Dou |
| author_sort | Yanyan Gu |
| collection | DOAJ |
| description | Station-level ridership is an important indicator for understanding the relationship between land use and rail transit, which is crucial for building more sustainable urban mobility systems. However, the nonlinear effects of the built environment on metro ridership, particularly concerning temporal heterogeneity, have not been adequately explained. To address this gap, this study proposes a versatile methodology that employs the eXtreme gradient boosting (XGBoost) tree to analyze the effects of factors on station-level ridership variations and compares these results with those of a multiple regression model. In contrast to conventional feature interpretation methods, this study utilized Shapley additive explanations (SHAP) to detail the nonlinear effects of each factor on station-level ridership across temporal dimensions (weekdays and weekends). Using Shanghai as a case study, the findings confirmed the presence of complex nonlinear and threshold effects of land-use, transportation, and station-type factors on station-level ridership in the association. The factor “Commercial POI” represents the most significant influence on ridership changes in both the weekday and weekend models; “Public Facility Station” plays a role in increasing passenger flow in the weekend model, but it shows the opposite effect on the change in ridership in the weekday model. This study highlights the importance of explainable machine learning methods for comprehending the nonlinear influences of various factors on station-level ridership. |
| format | Article |
| id | doaj-art-8302deb297ef4fdb80c735a4e0fe4044 |
| institution | OA Journals |
| issn | 2220-9964 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj-art-8302deb297ef4fdb80c735a4e0fe40442025-08-20T02:11:04ZengMDPI AGISPRS International Journal of Geo-Information2220-99642024-10-01131036510.3390/ijgi13100365Nonlinear and Threshold Effects on Station-Level Ridership: Insights from Disproportionate Weekday-to-Weekend ImpactsYanyan Gu0Mingxuan Dou1School of Statistics and Data Science, Ningbo University of Technology, Ningbo 315211, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaStation-level ridership is an important indicator for understanding the relationship between land use and rail transit, which is crucial for building more sustainable urban mobility systems. However, the nonlinear effects of the built environment on metro ridership, particularly concerning temporal heterogeneity, have not been adequately explained. To address this gap, this study proposes a versatile methodology that employs the eXtreme gradient boosting (XGBoost) tree to analyze the effects of factors on station-level ridership variations and compares these results with those of a multiple regression model. In contrast to conventional feature interpretation methods, this study utilized Shapley additive explanations (SHAP) to detail the nonlinear effects of each factor on station-level ridership across temporal dimensions (weekdays and weekends). Using Shanghai as a case study, the findings confirmed the presence of complex nonlinear and threshold effects of land-use, transportation, and station-type factors on station-level ridership in the association. The factor “Commercial POI” represents the most significant influence on ridership changes in both the weekday and weekend models; “Public Facility Station” plays a role in increasing passenger flow in the weekend model, but it shows the opposite effect on the change in ridership in the weekday model. This study highlights the importance of explainable machine learning methods for comprehending the nonlinear influences of various factors on station-level ridership.https://www.mdpi.com/2220-9964/13/10/365metro ridershipXGBoostnonlinear effectstemporal heterogeneity |
| spellingShingle | Yanyan Gu Mingxuan Dou Nonlinear and Threshold Effects on Station-Level Ridership: Insights from Disproportionate Weekday-to-Weekend Impacts ISPRS International Journal of Geo-Information metro ridership XGBoost nonlinear effects temporal heterogeneity |
| title | Nonlinear and Threshold Effects on Station-Level Ridership: Insights from Disproportionate Weekday-to-Weekend Impacts |
| title_full | Nonlinear and Threshold Effects on Station-Level Ridership: Insights from Disproportionate Weekday-to-Weekend Impacts |
| title_fullStr | Nonlinear and Threshold Effects on Station-Level Ridership: Insights from Disproportionate Weekday-to-Weekend Impacts |
| title_full_unstemmed | Nonlinear and Threshold Effects on Station-Level Ridership: Insights from Disproportionate Weekday-to-Weekend Impacts |
| title_short | Nonlinear and Threshold Effects on Station-Level Ridership: Insights from Disproportionate Weekday-to-Weekend Impacts |
| title_sort | nonlinear and threshold effects on station level ridership insights from disproportionate weekday to weekend impacts |
| topic | metro ridership XGBoost nonlinear effects temporal heterogeneity |
| url | https://www.mdpi.com/2220-9964/13/10/365 |
| work_keys_str_mv | AT yanyangu nonlinearandthresholdeffectsonstationlevelridershipinsightsfromdisproportionateweekdaytoweekendimpacts AT mingxuandou nonlinearandthresholdeffectsonstationlevelridershipinsightsfromdisproportionateweekdaytoweekendimpacts |