Estimation of a Weekend Mode Choice Model for Calgary
Bibliographic record
Abstract
The mode choice model presented here is part of a larger tour-based, activity-based modeling system. Tour groups are formed, and in some cases (for tours with a clear primary purpose, such as work tours) a primary destination is chosen. A tour mode logit choice model then selects an overall mode for the tour from three options ? Auto, Bicycle and Other (including transit, walk and being a passenger in another vehicle). The tour choice model is based on group size and composition, availability of a car to the group, and tour purpose. For tours with a primary destination, the travel disutility to this destination, and the accessibility at this destination are also considered. For tours without a clear primary destination (such as shopping tours), the accessibility at the home location is used. Once a tour mode is chosen, individual stop locations are selected and a logit choice model selects a mode for each trip on the tour. Tours made by Auto or Bicycle are restricted to the chosen mode, and no further model is needed. For tours made by the Other mode, each trip presents a choice between Walk, Transit and Passenger. The trip mode choice model uses group size and composition, auto availability, and tour purpose as well as specific travel costs of the three modes. This paper presents full estimation results for these models, including a discussion of the implications of the estimation results, permitting new insights into weekend travel behavior.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| 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 | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".