Modeling Daily Activity Program Generation Considering Within-Day and Day-to-Day Dynamics in Activity-Travel Behavior
Bibliographic record
Abstract
This paper presents Kuhn-Tucker demand system models for daily activity program generation. The models are for day-specific activity program generations of a week-long modeling span. The models accommodate within-day and day-to-day dynamics in time-use and activity-travel behavior explicitly. The activity types considered are the non-skeletal and flexible activities. These activities are divided into 15 generic categories. Under the daily time budget and non-negativity of participation rate constraints, the models predict the optimal set of activities (given the average duration of each activity type). The daily time budget considers the at-home basic needs and night sleep activities as a composite activity. The concept of composite activity ensures the behavioral dimension of time allocation and activity/travel behavior in a sense that the activities corresponding to the composite activity are regular skeletal activities but highly flexible in nature. We are sure to execute these activities but do not often allocate precisely a specific amount of time to them during advanced planning. Workers? total working hours (skeletal activity and not a part of the time budget) are considered as a variable in the models to accommodate the scheduling effects inside the generation model. The incorporation of previous day?s total executed activities as variables introduces day-to-day dynamics into the activity program generation models. The possibility of zero frequency of any specific activity under consideration is ensured by the Kuhn-Tucker optimality condition used. The models use the concept of goal/direct utility of activity episodes. The empirical estimations of the models are done using 2002-2003 CHASE survey data collected in Toronto. The models perform well in terms of fitting the observed data.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".