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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2412.03582 |
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| _version_ | 1866915049195438080 |
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| author | Zafri, Niaz Mahmud Zhang, Ming |
| author_facet | Zafri, Niaz Mahmud Zhang, Ming |
| contents | This study investigates the dynamic relationship between the built environment and travel in Austin, Texas, over a 20-year period. Using three waves of household travel surveys from 1997, 2006, and 2017, the research employs a repeated cross-sectional approach to address the limitations of traditional longitudinal and cross-sectional studies. Methodologically, it integrates machine learning and inferential modeling to uncover non-linear relationships and threshold effects of built environment characteristics on travel. Findings reveal that the built environment serves as a sustainable tool for managing travel in the long term, contributing 50% or more to the total feature importance in predicting individual travel-surpassing the combined effects of personal and household characteristics. Increased transit accessibility, local and regional destination accessibility, population and employment density, and diversity significantly reduce travel, particularly within their identified thresholds, though the magnitude of their influence varies across time periods. These findings highlight the potential of smart growth policies-such as expanding transit accessibility, promoting high-density and mixed-use development, and discouraging single-use development and peripheral sprawl-as effective strategies to reduce car dependency and manage travel demand. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_03582 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Exploring Non-Linear Effects of Built Environment on Travel Using an Integrated Machine Learning and Inferential Modeling Approach: A Three-Wave Repeated Cross-Sectional Study Zafri, Niaz Mahmud Zhang, Ming Computers and Society Machine Learning Applications This study investigates the dynamic relationship between the built environment and travel in Austin, Texas, over a 20-year period. Using three waves of household travel surveys from 1997, 2006, and 2017, the research employs a repeated cross-sectional approach to address the limitations of traditional longitudinal and cross-sectional studies. Methodologically, it integrates machine learning and inferential modeling to uncover non-linear relationships and threshold effects of built environment characteristics on travel. Findings reveal that the built environment serves as a sustainable tool for managing travel in the long term, contributing 50% or more to the total feature importance in predicting individual travel-surpassing the combined effects of personal and household characteristics. Increased transit accessibility, local and regional destination accessibility, population and employment density, and diversity significantly reduce travel, particularly within their identified thresholds, though the magnitude of their influence varies across time periods. These findings highlight the potential of smart growth policies-such as expanding transit accessibility, promoting high-density and mixed-use development, and discouraging single-use development and peripheral sprawl-as effective strategies to reduce car dependency and manage travel demand. |
| title | Exploring Non-Linear Effects of Built Environment on Travel Using an Integrated Machine Learning and Inferential Modeling Approach: A Three-Wave Repeated Cross-Sectional Study |
| topic | Computers and Society Machine Learning Applications |
| url | https://arxiv.org/abs/2412.03582 |