Development of a household travel resource allocation model
Bibliographic record
Abstract
Households allocate their travel resources – vehicles, time, budget, and supervision – to accomplish activities while minimizing overall time and cost subject to a set of constraints – the duration and sequence of activities, and the need to provide transportation to dependent travellers. In this research, we develop and test a heuristic based approach to schedule activities using a cost (disutility) minimization objective. The model is evaluated by comparing predicted schedules generated by the heuristics to actual travel patterns reported by participant households. While the dataset is small – only 14 households are included – the model successfully identifies tours for households of various compositions and demographics. The research is important in the local context as the study area – the Region of Waterloo, Canada – is a largely auto-dependent metropolitan area that is building a 19km, $818M (CDN) Light Rail Transit (LRT) system intended to increase public transport use and influence land use change. The Region is amongst the smallest municipalities in North America to implement this infrastructure.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".