Understanding travel behavior and mode choice prediction for university commuters: insights from discrete choice models and machine learning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
• University commuters’ mode choice is analyzed using DCMs & ML techniques. • Effect of built-environment, socioeconomic, and travel info factors is explored. • The two models prioritize data differently: MNL on demographics, ML on travel data. • MNL and ML models complement each other, enhancing insights from data analysis. • Car ownership, household size, and income significantly affect transit and walk choice. While there has been a growing interest in studying the commuting behavior of university students using mode choice models, most existing studies still lack the in-depth analysis needed to fully understand the factors influencing their transportation mode choices. To address this gap, this study examines the effects of various built-environment factors, socio-economic factors, and travel information to explore the commuting behavior of university commuters. Various prediction models based on discrete choice models (DCMs) and machine learning (ML) techniques are developed to predict the travel mode choice of university commuters. The developed prediction models are tested using a Commuting Habits survey conducted at a Canadian university. The results indicate that for transit and walk, household attributes such as car ownership, household size, and income level have significant impacts on choosing these modes as primary modes of transportation. It was also observed that car ownership and availability of transit passes are the most influential factors. As such, policymakers can further examine these relationships to incentivize commuters to promote sustainable transportation modes. Comparing DCM and ML-based models to predict the mode choice of university commuters, our analysis revealed that although the multinomial logit discrete choice (MNL) model resulted in lower performance in predicting commuters’ travel mode choices, it provides more insightful information regarding each specific mode of transportation. ML models prioritize information differently when making predictions, with MNL placing more emphasis on socio-demographic variables and household characteristics of respondents, whereas ML algorithms rely more on revealed travel-related variables.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it