Mixed Transport Network Prioritization Based on Environmental Impact and Population Density
Bibliographic record
Abstract
Today, due to the growing importance of sustainable development in urban areas, the decision to prioritize different transportation options in an area where users have to combine different transportation modes has received much attention in the scientific community. In this research, different modes of transportation including pedestrians, taxis, buses, and bicycles are considered as a combination of two different transportation modes in Tehran’s densely populated area. With the aim of prioritizing travel options in order to achieve sustainable urban development, public transport users were first asked about their travel means preferences. Next, by obtaining the opinions of urban transportation experts of Tehran Municipality regarding sustainable development of urban transportation, a set of transportation options in this area were prioritized on the basis of some criteria optimized using the analytic hierarchy process (AHP) method. The results indicate that air pollution and noise pollution, with the score of 0.33 and 0.24, respectively, were the two most important criteria for choosing a trip mode in dense population areas according to the opinion of transportation and traffic experts of Tehran. The AHP analysis indicated that the use of combined bicycle-walk mode with a score of 0.282 is the most preferred option, while bus-bicycle mixed mode with a score of 0.116 is the least preferred. Survey data indicate that there is a significant difference between people’s general preferences in choosing their urban transportation options and the sustainable urban development approach.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".