Investigating the Occupants' Behaviors and Perceptions Concerning the Sustainable Transportation System in Tehran City
Bibliographic record
Abstract
A transport crisis of major proportions is looming on the horizon in many of the world's cities. Road travel speeds during peak hours have already fallen below that of horse-drawn carriages, popular 80 years ago. Increasing motorization and air pollution threaten economic development as well as the environment. However, opportunities exist to solve these problems by limiting the demand for transport without affecting socio- economic development. An efficient and equitable urban transport policy can achieve its objectives by using a combination of new investment, economic incentives as well as adequate planning and regulatory measures. This will reduce overall travel and ensure the travel that occurs is safe, efficient and environmentally sustainable, as it will be discussed in this article. This paper investigates how sustainable transportation concerns systems, policies, and technologies and also its aims for the efficient transit of goods and services, and sustainable freight and delivery systems. The results show that there are important key issues in sustainable transportation such as Access not mobility; Moving people not cars; Reclaim city space for walking and pedaled vehicles and Stop subsidizing private motor vehicle. Besides the design of vehicle-free city planning, along with pedestrian and bicycle friendly design of neighborhoods is a critical aspect for grassroots activities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".