Indicators for Active Transportation in Tier II Indian Cities: A Case of Bhopal, India
Bibliographic record
Abstract
For a developing country to flourish sustainably, the transport sector needs to be balanced yet compete with its peers to support the growth of diverse sectors of the urban economy. Encouraging active mobility is one of the vital steps for the development of sustainable urban transportation. It indicates any mode of transport that involves physical activity, for example, cycling, walking, skateboarding and skiing. This paper is an attempt to identify the performance indicators that majorly affect the walkability and cyclability of people in cities capable of promoting active mobility. The objective is to corroborate the presence of qualitative and quantitative indicators in various sustainable transportation practices. Based on analytical hierarchy process, modified Delphi approach and user perception survey were utilised for the identification of performance indicators for Bhopal city. The indicators were segregated using exploratory factor analysis into five dimensions to categorise the performance indicators: sociodemographic, socioeconomic, physical and built environment and safety. It was found that supportive facilities were crucial for developing existing land use, physical and built environment and safety for users in a beginner city wanting to encourage users to switch to active modes choices. It was also found that urban design and built environment were the most influential factors which affect the various performance indicators for the establishment of active mobility modes for sustainable urban transportation.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".