Traffic Data Fusion Using SCAAT Kalman Filters
Bibliographic record
Abstract
For a particular section of a road network, at any point in time, there could be multiple sources of quantitative and qualitative traffic information available. Quantitative sensors are usually hardware-based and include loop detectors and GPS devices that produce numerical data. Qualitative sensors are usually processed data and include the traffic department's websites and radio broadcasts that produce subjective categorical data based on hidden processes. Each sensor is characterized by a specific level of error and sampling frequency. It is a challenge to combine and utilize multiple sourced data for estimating real-time traffic conditions. In the first phase of this research, by using single-constraint-at-a-time (SCAAT) Kalman filters, this paper combines multiple data sources of a section of a highway in Toronto as soon as any single sensor becomes active and estimates the current traffic conditions. However, in real-life, true traffic conditions are unknown because all sensors have associated errors with them. A micro-simulation package is used in order to have access to the true traffic conditions of a simulated environment that has been calibrated for a particular road section in Toronto. Then, the performance of the developed SCAAT filters are compared with the true traffic conditions under different sampling strategies with varying number of probes and varying sampling frequencies of sensors. The use of SCAAT filters is found to be an effective method to simultaneously fuse the data and estimate current traffic conditions.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".