On the Effect of COVID-19 on Drivers’ Behavior: A Survey Study
Bibliographic record
Abstract
COVID-19 had a disruptive effect on the global community. This study looks at the effects that the stringent lockdown measures enacted in March 2020 had on motorists' driving patterns. In particular, given the greater portability of remote working associated with the drastic decline in personal mobility, it is hypothesized that these may have served as accelerators for distracted and aggressive driving. To answer these questions, an online survey was conducted in which 103 respondents were asked to report on their own and other drivers' driving behavior. While respondents agreed they drove less frequently, they also indicated that they were not prone to more aggressive driving or engaging in potentially distracting activities whether for work or personal purposes. When asked to report on other motorists' behavior, however, respondents indicated they had witnessed more aggressive and distracting drivers on the road after March 2020 relative to the time before the pandemic. These findings are reconciled with the existing literature on self-monitoring and self-enhancement bias, and the existing literature on the effect of comparable large-scale, disruptive events on traffic patterns is used to discuss the hypothesis on how driving patterns may change after the pandemic.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".