Abstract
Designing effective built environment policies to reduce auto use is key to promoting sustainable transportation in suburban areas. However, most studies on the association between the built environment and auto use focus on the entire region rather than suburban areas. In addition, previous studies often ignore the possible nonlinear association between them. Applying Gradient Boosting Decision Trees to the data in the Twin Cities, USA, this study explores the nonlinear relationships between built environment attributes and driving distance in suburban areas and illustrates how the relationships differ from those in urban areas. The results show that suburban residents are less sensitive to the built environment than urban residents. More importantly, built environment policies that work in urban areas might be infertile in suburban areas. Although many studies have advocated population densification and mixed-use development for driving mitigation, this study suggests that these policies are ineffective in suburban areas. Instead, promoting job accessibility and densifying intersection density are promising to reduce auto use in suburban areas. Densifying transit stops has a small but nontrivial contribution to mitigating auto use.
| Original language | English (US) |
|---|---|
| Article number | 103924 |
| Journal | Transportation Research Part A: Policy and Practice |
| Volume | 179 |
| DOIs | |
| State | Published - Jan 2024 |
Bibliographical note
Publisher Copyright:© 2023 Elsevier Ltd
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 11 Sustainable Cities and Communities
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SDG 17 Partnerships for the Goals
Keywords
- Land use
- Machine learning
- Nonlinear relationships
- Sprawl
- VMT
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