How Does Built Environment Affect Metro Trip Time of Elderly? Evidence from Smart Card Data in Nanjing
Understanding the determinants of elderly people’s public transport usage patterns can offer new insights into elderly mobility issues and provide policy implications for planning toward an aging-friendly and sustainable city. However, few studies have examined the impact of the built environment on...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2022/2080552 |
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| _version_ | 1849414707530694656 |
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| author | Zhuangbin Shi Yang Liu Mingwei He Qiyang Liu |
| author_facet | Zhuangbin Shi Yang Liu Mingwei He Qiyang Liu |
| author_sort | Zhuangbin Shi |
| collection | DOAJ |
| description | Understanding the determinants of elderly people’s public transport usage patterns can offer new insights into elderly mobility issues and provide policy implications for planning toward an aging-friendly and sustainable city. However, few studies have examined the impact of the built environment on the trip time of the elderly using big data. Moreover, the elderly’s trip time has been mostly investigated by the multivariate linear regression model (MLR), ignoring the non-linear association between explanatory variables and trip time. Using smart card data from Nanjing in 2019, this study employs a gradient boosting regression trees (GBRT) model to probe into the correlations between the built environment and the elderly’s trip time. The results show that significant non-linear relationships exist between trip time and the selected explanatory variables, which cannot be captured by the MLR model. It suggests that relevant policy implementations should be carried out in conjunction with the elderly’s travel environment by regarding their threshold effects. Besides, interaction effects of spatial attributes on trip time are identified in our study. For example, elderly people living in the exurban area are more likely to take long-distance metro travel for their physical exercise. These findings demonstrate that planners and policymakers should not only consider one single built-environment factor, but also the interactions of various factors to enhance elderly mobility. |
| format | Article |
| id | doaj-art-6bd1f764dc994e0a83553583fe103513 |
| institution | Kabale University |
| issn | 2042-3195 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-6bd1f764dc994e0a83553583fe1035132025-08-20T03:33:45ZengWileyJournal of Advanced Transportation2042-31952022-01-01202210.1155/2022/2080552How Does Built Environment Affect Metro Trip Time of Elderly? Evidence from Smart Card Data in NanjingZhuangbin Shi0Yang Liu1Mingwei He2Qiyang Liu3Faculty of Transportation EngineeringFaculty of Transportation EngineeringFaculty of Transportation EngineeringSchool of Urban Planning and DesignUnderstanding the determinants of elderly people’s public transport usage patterns can offer new insights into elderly mobility issues and provide policy implications for planning toward an aging-friendly and sustainable city. However, few studies have examined the impact of the built environment on the trip time of the elderly using big data. Moreover, the elderly’s trip time has been mostly investigated by the multivariate linear regression model (MLR), ignoring the non-linear association between explanatory variables and trip time. Using smart card data from Nanjing in 2019, this study employs a gradient boosting regression trees (GBRT) model to probe into the correlations between the built environment and the elderly’s trip time. The results show that significant non-linear relationships exist between trip time and the selected explanatory variables, which cannot be captured by the MLR model. It suggests that relevant policy implementations should be carried out in conjunction with the elderly’s travel environment by regarding their threshold effects. Besides, interaction effects of spatial attributes on trip time are identified in our study. For example, elderly people living in the exurban area are more likely to take long-distance metro travel for their physical exercise. These findings demonstrate that planners and policymakers should not only consider one single built-environment factor, but also the interactions of various factors to enhance elderly mobility.http://dx.doi.org/10.1155/2022/2080552 |
| spellingShingle | Zhuangbin Shi Yang Liu Mingwei He Qiyang Liu How Does Built Environment Affect Metro Trip Time of Elderly? Evidence from Smart Card Data in Nanjing Journal of Advanced Transportation |
| title | How Does Built Environment Affect Metro Trip Time of Elderly? Evidence from Smart Card Data in Nanjing |
| title_full | How Does Built Environment Affect Metro Trip Time of Elderly? Evidence from Smart Card Data in Nanjing |
| title_fullStr | How Does Built Environment Affect Metro Trip Time of Elderly? Evidence from Smart Card Data in Nanjing |
| title_full_unstemmed | How Does Built Environment Affect Metro Trip Time of Elderly? Evidence from Smart Card Data in Nanjing |
| title_short | How Does Built Environment Affect Metro Trip Time of Elderly? Evidence from Smart Card Data in Nanjing |
| title_sort | how does built environment affect metro trip time of elderly evidence from smart card data in nanjing |
| url | http://dx.doi.org/10.1155/2022/2080552 |
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