An Impact Assessment of Cordon Pricing Relaxation on Modal Shift During the COVID-19 Pandemic
Bibliographic record
Abstract
The COVID-19 pandemic brought significant disruptions to transportation patterns worldwide, with a notable increase in Private Vehicle (PV) usage. Many studies have focused on travel restrictions, while neglecting to consider potential changes in transportation-related policies implemented during COVID-19 that may have improved public health. The present study explores the motivations for modal shifts to PVs and tries to analyze which of the risks of exposure and modifications in transportation policies has caused these modifications. Focusing on the case study of Tehran, Iran, where cordon pricing was relaxed during the pandemic, the study collected data through online and paper-based surveys from 1475 respondents. Binary logit models were employed to analyze the data and understand the impact of destination location, residency area, trip purpose, trip frequency, and vehicle characteristics on modal shift behavior. In addition to COVID-19 exposure as a primary reason for the modal shift, respondents also identified the relaxation of cordon pricing restrictions as a factor that increased the utility of PVs. The study's findings contribute to better understanding the dynamics of travel behavior during pandemics, guiding policymakers in devising effective strategies to address both public health concerns and traffic conditions.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".