Design, Validation, and Comparative Analysis of a Private Bus Location Tracking Information System
Bibliographic record
Abstract
This paper addresses various aspects related to the design, development, and validation of a web-based information system that is intended to facilitate the management of a bus transportation service offered by a Jordanian university to its staff and students. Passengers can use this system to track bus trips to find out how far a desired bus is from a specific location. Also, they can know about arrivals and departures of buses managed using this system. Specifically, this work explores UI design, data structures, database design, system architecture, and development methods to realize the required features (e.g., user roles, bus setup, driver assignment, bus routes, bus schedules, and trip monitoring) in the proposed bus location tracking system. It also suggests using the free open-source API, rather than the proprietary Google Maps API, to develop the interactive maps. The system also records trip information and solicits passenger feedback to allow reviewing and analyzing that data to enhance the quality of service, reduce operation cost, and improve passenger satisfaction. The conducted comparative analysis results illustrate that the open-source API is accurate, fast, and responsive similar to the proprietary API. Furthermore, the user survey output confirms that the deployed system is easy to use, helpful, fast, responsive, and accurate.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".