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Record W3106696364 · doi:10.1155/2020/8895927

Design, Validation, and Comparative Analysis of a Private Bus Location Tracking Information System

2020· article· en· W3106696364 on OpenAlexvenueno aff
Feras Al‐Hawari, Mohammed Al-Sammarraie, Taha Al-Khaffaf

Bibliographic record

VenueJournal of Advanced Transportation · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicTransportation Planning and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceService (business)TRIPS architectureArchitectureTracking systemAutomatic vehicle locationSystems designPublic transportTrack (disk drive)Transport engineeringEngineeringGlobal Positioning SystemOperating systemSoftware engineering

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.895
Threshold uncertainty score0.317

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.040
GPT teacher head0.297
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2020
Admission routes1
Has abstractyes

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