Effect of COVID-19 restrictions and fuel prices on traffic volume and offenses in Iran: A spatiotemporal analysis
Bibliographic record
Abstract
With the outbreak of the COVID-19 pandemic and the subsequent imposition of mobility restrictions in many nations, traffic volumes and driving behaviors have changed worldwide. This study aims to investigate the effect of COVID-19 restrictions and fuel prices on traffic volume and offenses (speeding, tailgating, and illegal overtaking) in Iran's provincial and aggregated data in the study period of March 21, 2019, to May 20, 2020. A time-series analysis was conducted to capture the effects of interventions in level and trend, followed by a spatial autocorrelation of the interventions among provinces to identify the provinces that formed clusters in terms of traffic volume and offenses before and after each intervention. Most of the COVID-19 restrictions (and the pandemic itself) did not reduce traffic volume and rate of traffic offenses whereas an increase in fuel prices decreased traffic volume and offenses (except for illegal overtaking). Furthermore, traffic volume showed an increasing trend after the imposition of mobility restrictions, suggesting that preventive measures could not control intercity trips during the pandemic. Spatiotemporal analysis showed mobility restrictions effectively removed some provinces from the clusters with above-average volume, tailgating, and overtaking data. The possible reasons for these findings and potential solutions are discussed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".