Towards Smart Mobility: Journey Reconstruction for Frictionless Public Transit using GPS and GTFS Data
Bibliographic record
Abstract
This paper delivers a state-of-the-art survey on frictionless travel for on-the-road public transportation using Global Position System (GPS) data. Different Automatic Fare Collection (AFC) approaches and the importance of journey reconstruction in public transit for accurate route and fare generation and, consequently, route and fare correction are explained. A journey reconstruction engine using the passenger's GPS and real-time General Transit Feed Specification (GTFS) data from the city public transit API is developed for the Check-In/Check-Out (CICO) ticketing approach. In addition, an intuitive GPS fetching time selection is explained to decrease battery drain while using the proposed architecture. Furthermore, different scenarios for collecting data in Hamilton, ON, Canada, are defined to study the effectiveness of the proposed system. To validate the model, data was collected using our developed transportation application, which enables travellers to check in and check out upon boarding and disembarking the bus. The routes were compared using real- time GTFS data to determine the passenger's transit services and accurately reconstruct their journeys. The performance of the journey reconstruction engine was assessed across multiple scenarios, with future directions for the research also explored.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".