Joint or Solo: Structural Equations Model of Household Activity Time Allocation Patterns
Bibliographic record
Abstract
The activity travel patterns of individuals within a household are often inter-related, and the realistic modeling of activity-travel behavior requires that these interdependencies be explicitly accommodated. A structural equations model of daily time allocation is developed and applied to analyze activity patterns by household heads. The model is unique in its simultaneous representation of each decisionmaker's decisions concerning independent and joint activity participation patterns, and the structural relationships among such patterns, household and individual socio-demographics, and individual's commuting time. Within this model, intra-household interactions can be explicitly explored by differentiating joint activities from independent activities by two household heads (husband and wife), based upon flexible criteria and restrictive criteria respectively. Simply put, the model attempts to capture intra-household interactions and intra-person interactions simultaneously, both of which are dependent upon household and individual socio-demographics. Data for individual household heads are from the 2002-2003 Toronto Travel-Activity Panel Survey (TTAPS) data. The findings suggest that there is a significant trade-off between independent and joint out-of-home activities. The negative impacts of presence of children and higher car ownership levels on joint activities are as expected. One interesting finding is that males who work-at-home are more inclined to take part in out-of-home joint activities. By comparing the two models using flexible and restrictive criteria, we find that the overall performance of the flexible model is better than the restrictive model. Further, the flexible model is able to capture more relationships
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".