Analyzing walking route choice through built environments using random forests and discrete choice techniques
Open accessAbstract
Walking is a form of active transportation with numerous benefits, including better health outcomes, lower environmental impacts and stronger communities. Understanding built environmental associations with walking behavior is a key step towards identifying design features that support walking. Human mobility data available through GPS receivers and cell phones, combined with high resolution walkability data, provide a rich source of georeferenced data for analyzing environmental associations with walking behavior. However, traditional techniques such as route choice models have difficulty with highly dimensioned data. This paper develops a novel combination of a data-driven technique with route choice modeling for leveraging walkability audits. Using data from a study in Salt Lake City, UT, USA, we apply the data-driven technique of random forests to select variables for use in walking route choice models. We estimate data-driven route choice models and theory-driven models based on predefined walkability dimensions. Results indicate that the random forest technique selects variables that dramatically improve goodness of fit of walking route choice models relative to models based on predefined walkability dimensions. We compare the theory-driven and data-driven walking route choice models based on interpretability and policy relevance.
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Mobile phone sensors provide new means to understand personal travel demand and urban topology, such as walking route choices.
“Also, mobile phone sensors provide new means to understand personal travel demand and the urban topology, such as walking route choices [795].”
From Tackling climate change with machine learning (2023) · cites this paper as [795]
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- Authors
- Tribby, C. P., Miller, H. J., Brown, B. B., Werner, C. M., & Smith, K. R.
- Affiliations
- The Ohio State University (US)
- University of Utah (US)
- Corresponding author
- Tribby, Calvin P
- Venue
- Environment and Planning B: Urban Analytics and City Science
- Publisher
- SAGE Publications
- Published
- 2016-07-20
- Volume
- 44
- Issue
- 6
- Pages
- 1145-1167
- ISSN
- 2399-8083 (ISSN-L)
- 2399-8091
- PMC
- PMC5754026
- OpenAlex
- W2478851384
- Cited by
- 76
- References
- 79
- Citations by year
- 2026: 8 · 2025: 10 · 2024: 10 · 2023: 11 · 2022: 10 · 2021: 8 · 2020: 4 · 2019: 5 · 2018: 7 · 2017: 3
- FWCI
- 8.23
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- Environment and Planning B Urban Analytics and City Science (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- National Cancer Institute (R01CA157509)
- Topics
- Social Sciences › Transportation › Urban Transport and Accessibility
- Physical Sciences › Engineering › Building and Construction › Urban Design and Spatial Analysis
- Social Sciences › Transportation › Transportation Planning and Optimization
- Keywords
- built environment, walkability, random forest, feature selection, discrete choice models, active travel, GPS mobility data, model interpretability, urban design
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