Users’ Evaluation of Rail Systems in Mass Events
Bibliographic record
Abstract
Each year during the ninth month of the Muslim lunar calendar, more than 2 million Muslim pilgrims from around the world travel to the Holy City of Mecca in Saudi Arabia for Hajj, an annual religious pilgrimage. A significant milestone in the effort to improve the existing transport system in the Holy City was the introduction of the Southern Masha'er Rail Line during the 2010 pilgrimage season. In the first year of operation, the line operated at only 30% of its full capacity before full implementation in the following year, when the line operated at full capacity of 72,000 passengers per hour. Results are presented of a users’ survey that aimed to assess the performance of the rail line from the perspective of its users. The analysis revealed that rail users faced longer access, waiting, and egress times compared with regular rail operations standards. However, survey results showed that the majority of pilgrims found these times to be tolerable. Moreover, the majority of users found the rail line and its stations to be of excellent quality and gave positive recommendations for using the rail line in the future. The analysis also produced some interesting observations that may be of relevance to rail operation in similar crowded events. Those observations are highlighted.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".