A comparative study of machine learning classifiers for modeling travel mode choice
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Supervised ML based on survey data can improve passenger mode choice models.
“Some recent studies have shown that supervised ML based on survey data can improve passenger mode choice models [322,571,597].”
From Tackling climate change with machine learning (2023) · cites this paper as [322]
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Tackling climate change with machine learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336 · see citing works
- Authors
- Hagenauer, J., & Helbich, M.
- Affiliations
- Leibniz Institute of Ecological Urban and Regional Development (DE)
- Utrecht University (NL)
- Corresponding author
- Hagenauer, Julian
- Venue
- Expert Systems with Applications
- Publisher
- Elsevier BV
- Published
- 2017-07
- Volume
- 78
- Pages
- 273-282
- ISSN
- 0957-4174 (ISSN-L)
- OpenAlex
- W2587802550
- Cited by
- 424
- References
- 96
- Citations by year
- 2026: 50 · 2025: 80 · 2024: 61 · 2023: 51 · 2022: 61 · 2021: 52 · 2020: 29 · 2019: 23 · 2018: 13 · 2017: 4
- FWCI
- 28.28
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Expert Systems with Applications (publishedVersion)
- Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
- Utrecht University Repository (Utrecht University) (submittedVersion)
- Topics
- Social Sciences › Transportation › Transportation Planning and Optimization
- Social Sciences › Transportation › Urban Transport and Accessibility
- Physical Sciences › Engineering › Building and Construction › Traffic Prediction and Management Techniques
- Keywords
- mode choice, machine learning classifiers, random forest, support vector machine, multinomial logistic regression, feature importance, travel diary data, built and natural environment, weather conditions, bicycle trips, trip distance, travel behavior
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