Exploring the Nonlinear Effects of Built Environment on Bus-Transfer Ridership: Take Shanghai as an Example
In this paper, the nonlinear effects of the built environment on bus–metro-transfer ridership are explored, based on Shanghai metro data, with an extreme gradient-boosting decision-trees (XGBoost) model. It was found that the bus-network density had the largest influence on transfer ridership, contr...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/11/5755 |
| _version_ | 1797493977509789696 |
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| author | Ding Liu Wuyue Rong Jin Zhang Ying-En (Ethan) Ge |
| author_facet | Ding Liu Wuyue Rong Jin Zhang Ying-En (Ethan) Ge |
| author_sort | Ding Liu |
| collection | DOAJ |
| description | In this paper, the nonlinear effects of the built environment on bus–metro-transfer ridership are explored, based on Shanghai metro data, with an extreme gradient-boosting decision-trees (XGBoost) model. It was found that the bus-network density had the largest influence on transfer ridership, contributing 27.56% predictive power for transfer ridership, followed by closeness centrality and bus-stop density, and their contribution rates are 21.6% and 17.27%, respectively. Local explanations for the model reveal the following conclusions: most built-environment variables have nonlinear and threshold effects on bus–metro ridership. The suggested values for the dominant contributors to bus–metro-transfer ridership are obtained. For example, bus-network density, bus-stop density, and closeness centrality were 12.8 km/sq. km, 11 counts/sq. km, and 0.18 km/sq. km, respectively, for maximizing bus–metro-transfer ridership. The interaction impacts of the bus–metro connection characteristics and the closeness centrality of metro stations on transfer ridership were, also, examined. The result showed that the setting of bus–metro-transfer facilities depended on the location of metro stations. It was necessary to improve the bus–metro-connection system, in metro stations with high closeness centrality. |
| first_indexed | 2024-03-10T01:27:44Z |
| format | Article |
| id | doaj.art-6c816112dcbb45a1b7db762e97b84bba |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T01:27:44Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-6c816112dcbb45a1b7db762e97b84bba2023-11-23T13:47:21ZengMDPI AGApplied Sciences2076-34172022-06-011211575510.3390/app12115755Exploring the Nonlinear Effects of Built Environment on Bus-Transfer Ridership: Take Shanghai as an ExampleDing Liu0Wuyue Rong1Jin Zhang2Ying-En (Ethan) Ge3College of Transport & Communications, Shanghai Maritime University, Shanghai 201306, ChinaCollege of Transport & Communications, Shanghai Maritime University, Shanghai 201306, ChinaCollege of Transport & Communications, Shanghai Maritime University, Shanghai 201306, ChinaCollege of Transport & Communications, Shanghai Maritime University, Shanghai 201306, ChinaIn this paper, the nonlinear effects of the built environment on bus–metro-transfer ridership are explored, based on Shanghai metro data, with an extreme gradient-boosting decision-trees (XGBoost) model. It was found that the bus-network density had the largest influence on transfer ridership, contributing 27.56% predictive power for transfer ridership, followed by closeness centrality and bus-stop density, and their contribution rates are 21.6% and 17.27%, respectively. Local explanations for the model reveal the following conclusions: most built-environment variables have nonlinear and threshold effects on bus–metro ridership. The suggested values for the dominant contributors to bus–metro-transfer ridership are obtained. For example, bus-network density, bus-stop density, and closeness centrality were 12.8 km/sq. km, 11 counts/sq. km, and 0.18 km/sq. km, respectively, for maximizing bus–metro-transfer ridership. The interaction impacts of the bus–metro connection characteristics and the closeness centrality of metro stations on transfer ridership were, also, examined. The result showed that the setting of bus–metro-transfer facilities depended on the location of metro stations. It was necessary to improve the bus–metro-connection system, in metro stations with high closeness centrality.https://www.mdpi.com/2076-3417/12/11/5755built environmenttransfer ridershipextreme gradient-boosting decision treenonlinear effectbus–metro connection characteristics |
| spellingShingle | Ding Liu Wuyue Rong Jin Zhang Ying-En (Ethan) Ge Exploring the Nonlinear Effects of Built Environment on Bus-Transfer Ridership: Take Shanghai as an Example Applied Sciences built environment transfer ridership extreme gradient-boosting decision tree nonlinear effect bus–metro connection characteristics |
| title | Exploring the Nonlinear Effects of Built Environment on Bus-Transfer Ridership: Take Shanghai as an Example |
| title_full | Exploring the Nonlinear Effects of Built Environment on Bus-Transfer Ridership: Take Shanghai as an Example |
| title_fullStr | Exploring the Nonlinear Effects of Built Environment on Bus-Transfer Ridership: Take Shanghai as an Example |
| title_full_unstemmed | Exploring the Nonlinear Effects of Built Environment on Bus-Transfer Ridership: Take Shanghai as an Example |
| title_short | Exploring the Nonlinear Effects of Built Environment on Bus-Transfer Ridership: Take Shanghai as an Example |
| title_sort | exploring the nonlinear effects of built environment on bus transfer ridership take shanghai as an example |
| topic | built environment transfer ridership extreme gradient-boosting decision tree nonlinear effect bus–metro connection characteristics |
| url | https://www.mdpi.com/2076-3417/12/11/5755 |
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