Missing Typical Weekdays in Travel Surveys: A Pseudo-Panel Approach to Explore Weekly Travel Patterns
Bibliographic record
Abstract
Travel surveys generally rely on single-day travel diaries where respondents report their travel information for a typical weekday. However, the concept of a typical weekday does not represent the current reality, as travel behavior has been largely altered in the post-pandemic period. Besides, the conclusions based on analyzing single-day travel diaries lack the ability to capture daily variations in travel behavior. In response to these concerns, this research proposed a framework to expand single-day travel diaries into longitudinal multi-day travel data using a pseudo-panel approach. Leveraging the constructed longitudinal data, the study evaluated the determinants of people’s daily participation in work–school, routine, and discretionary activities. Fixed and random effects panel data estimation models were used for this purpose. Results showed that activity participation is largely attributed to vehicle ownership, income, education, driving license, and household structure. Noticeable daily trends were observed in work–school and discretionary activities. A negative association between transit pass ownership and activity participation was noticed, suggesting social exclusion faced by transit users. In addition, teleworkers were found to be relatively more engaged in discretionary activities. Suburban residents were found to travel longer to participate in activities compared to urban dwellers. The proposed research framework can support future activity-based modeling aspects, such as activity participation, scheduling, mode choice, shared travel, and destination choice models, specifically addressing the “typical weekday” barrier.
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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 | 0.020 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".