Modeling significant factors affecting commuters’ perspectives and propensity to use the new proposed metro service in Doha
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Qatari government introduced a major public transport project titled the Doha Metro system to address the fast growing transportation demands in Qatar’s urban areas and to be ready for the Qatar 2022 FIFA World Cup. To benefit from this new metro system in reducing traffic congestion problems in Doha, it must be attractive with a reasonable level of service to attract large numbers of car users to switch to the new metro. This goal can be achieved by a better understanding of the user’s needs and expectations in Qatar. This paper aims to identify and quantify the significant factors affecting commuters’ perspectives, preferences and tendencies to use this new metro network for their daily trips in the future. The data used for the analysis was obtained from a self-reported questionnaire survey carried out among a sample of commuters living in Doha. Different data mining techniques were employed including conditional distributions and two-way analysis. In addition, logistic regression and structural equation modeling approaches were developed. The results revealed that the location of metro stations, the metro station’s features, the metro’s features, gender, the number of daily trips, the purpose of trips, and the average duration of trips in Doha were the significant factors that affected commuters’ willingness and tendency to use the new metro system. The results of this study provide authorities and decision makers in Doha with valuable insights that should be taken into consideration prior to implementing the new metro service to ensure its success.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it