Calibrating Bus Mobility Data for Bus-based Urban Vehicular Networks
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
In addition to being one of the primary means of transport, with the advent of sensing and communication technologies, buses belonging to the public transport system have gained a new role in urban centers. They have been applied as a powerful vehicular network that covers an entire city, called BUS-VANET. For the design and validation of solutions for this type of network, the nodes' mobility information is essential. For instance, data from the buses' GPS trajectories can be used to understand the dynamics of encounters between them. This knowledge can be applied to design applications and services for different users, besides providing the necessary information to properly manage this important public transport solution. However, real-world trajectories have several imperfections. In particular, GPS trajectories are heterogeneous, asynchronous, and typically contain a low sample rate. These characteristics impose certain limitations on the use of this dataset in the design of solutions for a BUS-VANET. In this work, we propose a hybrid method of calibrating trajectories based on historical information of trajectories and a road network to overcome these problems. We showed that our method surpasses the state-of-the-art techniques in several perspectives through evaluation with realistic data.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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