Interpolating Sparse GPS Measurements Via Relaxation Labeling and Belief Propagation for the Redeployment of Ambulances
Bibliographic record
Abstract
One major challenge for traffic management systems is the inference of traffic flow in regions of the network for which there are little data. In this paper, Global-Positioning-System (GPS)-based vehicle locator data from a fleet of 40–60 roving ambulances are used to predict the most likely ambulance speeds in a network of 20 000 streets in the city of Ottawa, ON, Canada. First, the road network is represented as a directed graph data structure. Then, we compare two algorithms, i.e., relaxation labeling and belief propagation, that interpolate the sparse and noisy measurements from the fleet to obtain dense locally consistent ambulance speeds. Unlike several other systems in the literature, we model all of the city's freeways and surface streets, and both road types are treated with equal importance. Furthermore, the data structure and algorithms described in this paper are not only extended to real-world needs such as road closures and the incorporation of live data with historical data but are also computationally efficient, providing updates in intervals of less than 5 min on commodity hardware. Presented experimental results address the key issue of validating the performance and reliability of the system.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".