ROAD MASS TRANSIT SYSTEM AND SERVICE DELIVERY IN ABUJA NIGERIA: ADOPTING WINNIPEG TRANSIT MODEL, CANADA
Bibliographic record
Abstract
This study aims to compare the road mass transit service delivery of Abuja Urban Mass Transit (AUMT) with that of Winnipeg Transit in Canada. The specific objectives are to determine the influence of bus-based system standards on the service delivery of both AUMT and Winnipeg Transit, and to assess the differences in the reliability and timeliness of services. This study employed mixed methods design, specifically the convergent parallel design on a population of Mass Transit firms Supervisors, Drivers and Commuters in Abuja and Winnipeg. Cochran sample size determination was used to determine a sample size of 386 for the study which was, for the purpose of generalizable and reliable research output, equitably distributed across the two groups in Abuja and Winnipeg. It was found that: Bus-based systems in road mass transit positively and significantly influenced service delivery of Winnipeg transit, Canada (β = 1.659; p-value = 0.006) while Bus-based systems in road mass transit positively but insignificantly influenced service delivery of Abuja Urban Mass Transit (β = 0.370; p-value = 0.648). The study also found that the degree of reliable and timely service in road mass transit service delivery significantly differred between Abuja Urban Mass Transit and Winnipeg Mass Transit, with Winnipeg Transit being perceived as more reliable and timely (p-value = 0.000); In conclusion, the effectiveness of road mass transit systems in service delivery is significantly influenced by infrastructure quality, technological integration, and operational efficiency, with Winnipeg Transit demonstrating superior performance compared to Abuja Urban Mass Transit. The study therefore recommends that the Federal Capital Territory, in collaboration with the Abuja Urban Mass Transit (AUMT) authorities, should undertake a comprehensive evaluation of the current bus-based system. It should focus on enhancing fleet management, ensuring better maintenance schedules, increasing bus availability during peak hours, and optimizing routes to improve overall service delivery, aiming for a more substantial impact on service quality similar to the results seen in Winnipeg
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".