Evaluation of the impact of Covid-19 on transport sustainability in Iran
Bibliographic record
Abstract
The Covid-19 outbreak changed travel behaviour in many ways. This paper evaluates these changes in Tehran, Iran, from a transportation sustainability perspective. It uses data from travel surveys before and during the pandemic to evaluate changes in travel activity and their impacts on public transportation (PT) system costs and revenues, air quality and traffic crashes. A structural equation model (SEM) is used to assess the potential impact of passengers’ characteristics, details of each transportation mode and the severity of Covid-19 on travel behaviour. Hypothesis testing is used to compare the changes that occurred in air quality and traffic crashes. SEM outputs indicate that the frequency of trips previously made by sustainable modes and changes in regular trips have the highest impact on the sustainability of trips made during the Covid-19 pandemic. Air quality declined in many city districts. The pandemic caused reductions in PT ridership and an increase in private car use, which threaten the long-term sustainability of public transit services. Crash fatalities declined slightly, particularly during periods of movement restrictions. However, motorcycle fatal crashes increased in comparison with the reduction in motorcycle trips, apparently due to increased traffic speeds. The government did not use the pandemic as an opportunity to promote sustainable and low-contagion modes. This analysis infers that the Covid-19 outbreak reduced the overall transportation sustainability in Tehran.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".