Bibliographic record
Abstract
Road safety has remained a primary issue worldwide since the advent of automobiles. Significant advances have been made over the past several decades that have led to substantial improvements in safety. These advances include changes to the infrastructure, improvements to automobiles including ambient interfaces, connected and automated technology, and the availability of advanced cognitive training interventions. However, the recent spread of COVID-19 and subsequent social distancing measures, including travel bans and partial/complete lockdowns worldwide, has caused a dynamic shift in driver behavior, particularly those elements of behavior most associated with safety in general and crashes in specific. The current article aims to identify the critical changes to safe driver behavior in the post-COVID-19 era, reflect upon the behavioral factors driving this change, and suggest potential countermeasures to mitigate the unexpected change in driver behavior. The current review identified three crucial characteristics of post-pandemic driver behavior consisting of two negative trends, including 1) an increase in speeding behaviors, and 2) a greater propensity for distracted driving, and one positive trend 3) a reduction in congestion. A recent literature review shows that critical behavior changes include increased excessive speeding accompanied by a reduction in congestion. Further, distracted driving incidents are rising globally, while road crashes and mobility have declined. A preliminary analysis was conducted on open traffic data available from various locations. In Ontario, the number of speeding tickets issued from July 2020 to June 2021 was more than double the number of tickets issued in 2019. Additionally, an analysis of traffic tickets issued in New York State showed an increase in violations involving mobile phones and portable electronic devices in driving during the lockdown period in 2020. This unexpected shift in driver behavior necessitates the exploration of countermeasures that promote safer driver behavior. A discussion is presented along with future steps to tackle the negative trends in driver performance. The findings may have potential implications for policymakers, researchers, and the public.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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 | 0.000 |
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".