Temporal variations in the non-linear relationships between metro ridership and the built environment: insights from interpretable machine learning using four-year data Article Swipe
Linchuan Yang
,
Ying’ao Peng
,
Jie Chen
,
Yanan Liu
,
Haosen Yang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1093/iti/liae023
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1093/iti/liae023
Understanding the association between metro ridership and the built environment is crucial for promoting integrated transportation and land use planning. However, prior research has rarely examined the temporally varying and/or non-linear associations between metro ridership and the built environment. To address this gap, this study collects metro ridership data in Chengdu, China, for January of each year between 2019 and 2022 and uses light gradient-boosting machine and SHapley Additive exPlanations models to examine the complex, non-linear associations between metro ridership and the built environment over 4 years. Our findings highlight the non-linear nature of the built environment’s influence. The key predictors remained relatively stable throughout the years, including the number of entrances (the top predictor across all years), employment density, and the floor area ratio. However, the influence of built environment factors, such as land-use mix, residential micro-district density, and distance to the city center, shows great temporal variations, underscoring the importance of incorporating temporal dynamics into analyses of the interactions between metro ridership and the built environment. This study offers a valuable reference for urban and transportation planners in crafting tailored policies for station-area transit-oriented development.
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- en
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- https://academic.oup.com/iti/advance-article-pdf/doi/10.1093/iti/liae023/61261760/liae023.pdf
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https://doi.org/10.1093/iti/liae023Digital Object Identifier
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Temporal variations in the non-linear relationships between metro ridership and the built environment: insights from interpretable machine learning using four-year dataWork title
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articleOpenAlex work type
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enPrimary language
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2024Year of publication
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2024-01-01Full publication date if available
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Linchuan Yang, Ying’ao Peng, Jie Chen, Yanan Liu, Haosen YangList of authors in order
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https://doi.org/10.1093/iti/liae023Publisher landing page
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https://academic.oup.com/iti/advance-article-pdf/doi/10.1093/iti/liae023/61261760/liae023.pdfDirect link to full text PDF
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://academic.oup.com/iti/advance-article-pdf/doi/10.1093/iti/liae023/61261760/liae023.pdfDirect OA link when available
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| abstract_inverted_index.density, | 120, 139 |
| abstract_inverted_index.distance | 141 |
| abstract_inverted_index.dynamics | 156 |
| abstract_inverted_index.examined | 26 |
| abstract_inverted_index.factors, | 132 |
| abstract_inverted_index.findings | 89 |
| abstract_inverted_index.land-use | 135 |
| abstract_inverted_index.planners | 179 |
| abstract_inverted_index.policies | 183 |
| abstract_inverted_index.remained | 102 |
| abstract_inverted_index.research | 23 |
| abstract_inverted_index.tailored | 182 |
| abstract_inverted_index.temporal | 148, 155 |
| abstract_inverted_index.valuable | 173 |
| abstract_inverted_index.entrances | 112 |
| abstract_inverted_index.highlight | 90 |
| abstract_inverted_index.including | 108 |
| abstract_inverted_index.influence | 128 |
| abstract_inverted_index.planning. | 20 |
| abstract_inverted_index.predictor | 115 |
| abstract_inverted_index.promoting | 14 |
| abstract_inverted_index.reference | 174 |
| abstract_inverted_index.ridership | 6, 35, 48, 80, 164 |
| abstract_inverted_index.employment | 119 |
| abstract_inverted_index.importance | 152 |
| abstract_inverted_index.influence. | 98 |
| abstract_inverted_index.integrated | 15 |
| abstract_inverted_index.non-linear | 31, 76, 92 |
| abstract_inverted_index.predictors | 101 |
| abstract_inverted_index.relatively | 103 |
| abstract_inverted_index.temporally | 28 |
| abstract_inverted_index.throughout | 105 |
| abstract_inverted_index.association | 3 |
| abstract_inverted_index.environment | 10, 84, 131 |
| abstract_inverted_index.residential | 137 |
| abstract_inverted_index.variations, | 149 |
| abstract_inverted_index.associations | 32, 77 |
| abstract_inverted_index.development. | 187 |
| abstract_inverted_index.environment. | 39, 168 |
| abstract_inverted_index.exPlanations | 70 |
| abstract_inverted_index.interactions | 161 |
| abstract_inverted_index.station-area | 185 |
| abstract_inverted_index.underscoring | 150 |
| abstract_inverted_index.Understanding | 1 |
| abstract_inverted_index.incorporating | 154 |
| abstract_inverted_index.micro-district | 138 |
| abstract_inverted_index.transportation | 16, 178 |
| abstract_inverted_index.environment’s | 97 |
| abstract_inverted_index.transit-oriented | 186 |
| abstract_inverted_index.gradient-boosting | 65 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/11 |
| sustainable_development_goals[0].score | 0.7699999809265137 |
| sustainable_development_goals[0].display_name | Sustainable cities and communities |
| citation_normalized_percentile.value | 0.20683575 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |