To bike or not to bike: Exploring cycling for commuting and non-commuting in Bangladesh
Bibliographic record
Abstract
In recent years, Bangladesh has started moving its transportation vision towards achieving sustainability goals such as increasing bicycle infrastructure, sidewalks, reducing air pollution, etc. To contribute to the ongoing discussion, we explored factors that influence the use of bicycles for different trip purposes in Rajshahi, a medium-sized city in Bangladesh. A face-to-face household survey was conducted to collect individuals’ socio-demographic characteristics, their travel patterns for different trip purposes, and perceptions of the built environment. We developed four Integrated Choice and Latent Variable (ICLV) models to understand the influence of latent perceptions on bicycling for commuting and non-commuting (i.e., grocery shopping, going for tea, and recreational) trips. The analysis indicates that women are more likely to choose a bike for commuting trips but are less likely to use bikes for recreational trips. The results also show that the choice of commuting by bicycle is positively associated with commuting distance and negatively associated with residential land use. Walkability perception has a significant positive association with the choice of bikes for commuting and non-commuting trips. Road safety perception for active travel is positively associated with bike choice for recreational trips, and crime perception of the neighborhood is negatively associated with bike choice for grocery trips. The results from this study will be helpful for policymakers to understand and improve the built environment to attract individuals towards bike use.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".