Analyzing Transit Stop Service Areas Using Geographic Information Systems (GIS): A Case Study of UET Taxila
Bibliographic record
Abstract
The University of Engineering and Technology Taxila (UET Taxila), established in 1975 as a campus of the University of Engineering and Technology Lahore, is a leading public university in Taxila, Punjab, Pakistan. With a transportation network spanning approximately 518 square kilometers across Islamabad, Rawalpindi, and Wah Cantt, it provides an economical and efficient transit system for students, faculty, and staff. This study evaluates the service area coverage of transit stops using Geographic Information Systems (GIS) to assess network efficiency, accessibility, and spatial gaps. At the university, 16 buses operate across 16 routes, serving an average of 810 users daily. The analysis defines transit stop service areas based on 5, 10, and 15-minute walking times, assuming a walking speed of 100 meters per minute. Results reveal that 69% of users access transit stops within a walkable distance, while 31% rely on other modes of transport. Among those with walking access, 27% reach a stop in under 5 minutes, 23% within 5 to 10 minutes, and 19% within 10 to 15 minutes. These findings provide valuable insights for optimizing the university's transportation network and supporting future transit planning decisions
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".