Joint Model of Participation in Nonwork Activities and Time-of-Day Choice Set Formation for Workers
Bibliographic record
Abstract
Nonwork activity and travel participation, an important component of overall travel demand, is complex to model because the greater degree of flexibility associated with such travel induces larger variability and randomness in the behavior. This paper aims to offer a framework for modeling workers’ participation in and travel mileage allocated to non-work activities during various time periods of the day. Five time-of-day blocks are defined for workers on the basis of the period of the day in relation to the work schedule. Individuals can choose to pursue nonwork activities in one or multiple time blocks and travel miles to accomplish the activities. A multiple discrete-continuous extreme value modeling approach is used to model this phenomenon. A unique element of the paper is the addition of a latent choice set generation model as a first component in the model system. This choice set generation model can be used to determine the set of time-of-day periods that each individual will consider for the pursuit of nonwork activities, while recognizing that the consideration choice set is not explicitly observed (and is therefore latent) by the analyst. Thus, the model system presented in this paper is capable of modeling nonwork activity engagement and associated travel mileage by time-of-day period while incorporating varying choice sets across individuals. The two-component model system is applied to a survey sample drawn from the San Francisco area in California and shown to perform substantially better than a pure multiple discrete-continuous extreme value model that assumes a constant choice set across the sample.
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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 | 0.004 | 0.007 |
| 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) | 0.005 | 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".