Modeling Household Weekend Activity Durations in Calgary
Bibliographic record
Abstract
This paper describes how a large-scale survey for household weekend activity and related travel was completed recently in the City of Calgary. The data include detailed information of travelers and activity, such as personal type (e.g., adult worker or senior), employment status (fulltime or part-time), annual income, gender, activity type (e.g., shopping or sociality), activity duration, and starting & ending time of each activity. A micro-simulation based choice behavior model has been used in the previous city planning tasks. The model is capable of simulating complete travel behavior of individuals by considering travel purpose, travel mode, itinerary, activity durations, and even group influences. Previously, the simulation was done using a Monte Carlo process with sampling distributions based on weighted sample of observed durations. Simulations based on such “static” distributions, however, can not be used to analyze the influences of various policies (e.g., changes in transit fare) and travel conditions (congestion or easier accessibility) to household activities in a dynamic environment. This study is an initiative for modeling the relationship between activity durations and various influencing factors (e.g., personal type, employment status, and income level, etc.). Especially, hazard and survival functions are specified for each type of activity and individual personal type. The results show a high degree of fit and it is believed that these models would be useful for travel-related policy analysis in the future modeling framework.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".