Population Synthesis based Pseudo Panel Modeling of Out-of-Home Discretionary Activity Duration
Bibliographic record
Abstract
This paper presents a novel approach of pseudo panel based duration modeling of discretionary activities. Pseudo panel observations are derived from a population synthesis method utilizing repeated cross-sections of Canadian time use data of 1992, 1998, 2005, and 2010. Duration models are estimated for four major out-of-home discretionary activities including shopping, recreational, social, and entertainment activities. Public-use micro-data has a small sample for each surveyed year. Hence, expansion of the sample is necessary to develop reasonable pseudo panel cohorts for duration model estimation. Therefore, this study uses a Multi-dimensional Population Synthesis (MPS) based approach to generate synthetic population for pseudo panel modeling. The sample size of 645, 672, 1105 and 962 have been expanded to 5% of the total population of Nova Scotia, Canada for each of the surveyed years. In order to capture panel effects, a Latent Class Accelerated Hazard (LCAH) model is considered. Model results provide significant behavioural insights for long-time span pseudo panels. For instance, individuals from different age groups and generations showed different activity pattern with regards to marital status, working status etc. Moreover, married individuals spend a longer duration in shopping activities but shorter duration in recreational activities. Models results also suggest that significant heterogeneity across latent classes exists. For instance, single individuals from Latent Class1 (LC1) spend a shorter duration in social activities whereas single individuals from Latent Class2 (LC2) spend a longer duration. The study demonstrates that the application of pseudo panel methodology can be utilized for exploring longitudinal dynamics of activity duration where panel travel survey is absent.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".