The Assessment of the Change in the Share of Public Transportation by Applying Demand Management Policies: Evidence from Extending Traffic Restriction to the Air Pollution Control Area in Tehran
Bibliographic record
Abstract
The growing car ownership has caused a lot of problems such as increased travel time and environmental pollution. In recent years, different policies have been proposed for travel demand management. Among these plans conducted in Tehran, the Odd-Even day plan starting from the door of each house or the extension of traffic congestion zone to the Odd-Even plan zone can be mentioned. In the present study, to determine the change in the behavior of the people traveling in the air pollution control area in Tehran in return for the payment of a various toll and exploring their pro-environmental beliefs and attitudes which supports the Value-Belief-Norm (VBN) theory, a stated preference questionnaire has been designed, and 500 of it were distributed among the individuals in this area and then collected. The results show that 51% of people have used their private cars to travel within the area. 24% of people have used semipublic transportation, and 25% have used public transportation (bus and subway) for their traveling. Based on the tolling design scenario, which was with an increase of 15 to 18% of the base traffic congestion zone prices of 2016, the relative frequency of using four types of non-public transportation (which is the sum of private vehicles and semipublic transportation) decreased 20 and 21% for different types of tolls throughout the day.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".