Cited by 424Transportation Planning and OptimizationUrban Transport and AccessibilityTraffic Prediction and Management Techniques

A comparative study of machine learning classifiers for modeling travel mode choice

Open access
Hagenauer, Julian, Helbich, Marco·2017·Expert Systems with Applications

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Registry

Crossref · 10.1016/j.eswa.2017.01.057

Cites

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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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Cites

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Research works printed but not linked: 3

  1. de Dios Ortúzar, J. , & Willumsen, L. G. (2011). Modelling transport . John Wiley & Sons .
  2. Kuhn, M. (2008). Caret package. Journal of Statistical Software, 28 (5) .
  3. Verplanken, B. , Aarts, H. , & Van Knippenberg, A. (1997). Habit, information acquisi- tion, and the process of making travel mode choices. European Journal of Social Psychology, 27 (5), 539-560 .

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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
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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