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 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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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 teacher head, 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".