Including Minor Modes of Transport in a Tour-Based Mode Choice Model with Household Interactions
Bibliographic record
Abstract
Mode choice models used for travel demand forecasting generally include “major” transportation modes of driving, ridesharing, walking, and riding public transit. In the Toronto Area, these make up 96% of all trips. This paper describes the challenge of realistically modeling “minor” modes, while maintaining behavioral realism in the rest of the model. This is critical to public policy since increasing the mode share of bicycling, commuter rail, and school bus has the potential to reduce emissions, save on expensive auto infrastructure, encourage healthier lifestyles, reduce congestion, and support liveable communities. The tour-based model presented in this paper simulates household interactions as part of the mode choice process. Model parameters are estimated using a choice-based sample of tours in the Toronto Area and a genetic algorithm. The model shows very good results for commuter rail and school bus modes, but limited success for the drive access subway, taxi, and bicycle modes. Representation of niche markets through restricted choice sets allows for a parsimonious utility function that includes level of service, land-use, activity, and socioeconomic variables.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".