How did mobility choices evolve in the first year of the COVID-19 pandemic? Identifying the factors affecting mobility choices: a case study in New York City
Bibliographic record
Abstract
This study aims to identify the factors affecting mobility choices (e.g., transit, car, and walk) during COVID-19 in New York City. Even though the COVID-19 pandemic evolved over time, most of the current studies heavily relied on cross-sectional datasets to understand individuals’ mobility choices. To address this gap, this study uses time-series and panel datasets and estimated aggregate and disaggregate level models to assess individuals’ mobility choices. The time-series model result reveals a negative correlation between public transit demand and grocery stores and residential activities during the pandemic. Also, walking demand increases with increased trips to transit stations, retail and recreational areas, and parks. The econometric model results show that employment status, reduced hours or pay cuts at the workplace, income, age, race, family size, and vehicle ownership are the factors that affect individuals’ mode-switching behavior during the pandemic. These models can be used for forecasting purposes in the post-pandemic era.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".