Parameter Calibration of Traffic Flow Speed-Density Model Based on K-means Clustering Algorithm and Least Square Method
Bibliographic record
Abstract
This paper originates from the objective to verify the effectiveness of the K-means clustering algorithm in dealing with large sample data deviation and better describes the characteristics of highway traffic flow. The data was downloaded from the open source data of traffic flow detector of Whitemud Drive Highway in Canada. After data cleaning, K-means Clustering Algorithm was used to cluster large sample data to solve the problem of data deviation. Then the parameter calibration of Greenshields and Underwood traffic flow speed-density models according to the training set after clustering was conducted based on Least Square Method. Results of the two models are evaluated by means of statistics and probability theory. Finally, evaluation results show that the fitting effect of the Greenshields model is better than the Underwood model, and it is more suitable for decribing the traffic flow state of the highway in the study section.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".