Spatial Variety in Weekly, Weekday-to-Weekend, and Day-to-Day Patterns of Activity-Travel Behavior: Initial Results from Toronto Travel-Activity Panel Survey
Bibliographic record
Abstract
Our current understanding of activity-travel processes has emerged largely from empirical study of short-run cross-sectional surveys. The behavioural accuracy of the short-run survey has been explored in past research, with an emphasis placed on non-spatial outcomes. In this paper, we use the first wave of the Toronto Travel-Activity Panel Survey (TTAPS) to explore an area of research that has received little attention; namely, the presence of spatial variety in activity-travel behaviour. We begin by looking at the extent to which individuals engage in spatially repetitive location choices during the course of a single week. We then use an area-based measure of activity dispersion to expose differences in the weekday-to-weekend and day-to-day behaviours of a case study household. Retrospective examination of all weekly activities revealed a level of spatial repetition that did not materialize for activities classified by purpose, access mode, and planning horizon. Despite the inherent spatial flexibility offered by the personal automobile, spatial repetition was found to be surprisingly similar across access modes. Lastly, results from our case study suggest the presence of weekday-to-weekend and day-to-day fluctuations in spatial properties of activity-travel patterns. Findings from this research challenge the efficacy of the short-run survey as a mechanism for capturing archetypal patterns of spatial behaviour. In addition, identification of temporal fluctuations in the spatial outcomes of activity-travel decisions suggests that we have more to learn about the stability of transport-land use interactions over time.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".