A policy and institutional analysis of urban transport system: the case of Pakistan’s Lahore in the context of COVID-19
Bibliographic record
Abstract
The bicycle is recognized as a sustainable mode of transport, yet in the developing world, its use is hindered by several factors. The COVID-19 pandemic, with its emphasis on isolation, created a changed travel pattern and thus an experimental environment for bicycling. This study examines Lahore, Pakistan, to evaluate bicycle promotion during and beyond the pandemic. It assesses policy frameworks, institutional implementation capacity, and the perception of cycling infrastructure and regulations based on the user feedback. Lahore, a major Pakistani city with significant development and capacity, has faced severe smog and poor air quality, highlighting the need for environmentally friendly transport. The study reveals an increase in bicycle use during COVID-19 restrictions, with over 96% of the respondents noting this rise. Many tried bicycling for the first time due to reduced traffic. However, post-restrictions opinions varied on whether the trend persisted. Better road infrastructure was found to corelate positively with the bicycling trend. Studies identified traffic lawlessness, high motorization, lack of infrastructure, smog, and harsh weather as major barriers. Despite the existence of civil society groups promoting bicycling, their efforts are hindered by lack of participation in policy and decision making. The study calls for addressing policy and institutional bottlenecks to promote bicycling in Lahore, with broader implications for Pakistan and other developing countries. Improved coordination among institutions and inclusion of user perspectives are crucial for creating a more bicycle friendly system.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".