How will individuals travel post-COVID? A statistical framework to identify the determinants of different travel behaviors
Bibliographic record
Abstract
ABSTRACT: The outbreak of the pandemic in 2020 has caused an unforeseen situation that significantly changed individuals’ travel patterns around the globe. The transmission prevention measures that were put in place have had both short- and long-term effects on people's activity systems and their daily travel patterns. In order to investigate the pandemic's effects on travel patterns and activity systems, a web-based questionnaire was developed and distributed in Montreal, Canada, in April and May of 2020. In addition to questions about activities before and during COVID-19, a section on how people anticipated travelling in the post-pandemic era was also included in the questionnaire. This research presents insights into how people are planning to travel in the post-COVID period utilizing K-means clustering and Multinomial Logit (MNL) models. the data gathered from the survey resulted into 1620 completed questionnaires, out of which 1597 were valid. This investigation offers crucial insights on anticipated changes in travel frequency, use of public transportation, and use of bicycles throughout the post-pandemic era. The findings of this study will help planners and policymakers create plans that will better prepare the cities for the post-pandemic era by taking into account the projected changes in people's travel patterns.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| 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".