Long Term, Pre, and Post Impacts of SARS-CoV-2 Pandemic on Road Traffic Crashes in the Case of Budapest, Hungary
Bibliographic record
Abstract
SARS-CoV-2 is a pandemic that affects road traffic flaw and crashes globally. This study attempted to compare the situation of road traffic crashes in the city of Budapest before and after the SARS-CoV-2 pandemic to better understand its long-term percussive effects. The study considers 12208 road traffic crashes that registered between 20 May 2018 – 31 December 2021. The rate and severity of road traffic crashes during the SARS-CoV-2 pandemic examined by using a percentage frequency distribution and a severity index. This study depicted that most crashes reported during the normal daytime between15:01-18:00 (peak hour). The study indicated that during the SARS-CoV-2 pandemic the road traffic crashes were reduced by 20.15%. A rear-end collision was one of the most common type of catastrophes highly registered. Road users, particularly drivers, heavily endorsed crashes. Even though the proportion of road traffic crashes caused by alcohol consumption was modest (6%), the rate of alcohol consumption and its concentration increased slightly during the SARS-CoV-2 pandemic. At the same time the number of crashes caused by high-speed traffic maneuvers reduced. Improper interpretation of road traffic signs, road pavement condition and failure to respect proper sight distance were influential reasons of road traffic crashes among the top. Meanwhile, the distributional impact of careless driving in the aftermath of the SARS-CoV-2 pandemic causes a shift in rank. Therefore, this study proved that during SARS-CoV-2 pandemics road traffic crashes reduced, the rate and concentration of alcohol consumption increased, and careless driving was encouraged.
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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".