A Simulation Approach to Detect Arterial Traffic Congestion Using Cellular Data
Bibliographic record
Abstract
Cellular data provide a promising way for congestion detection with low cost and high coverage, and the simulation study is a feasible solution to verify the detection method. This paper presents a simulation approach that uses cellular data to detect traffic congestion on urban arterials based on the relationship between cellular data and traffic status. The virtual testbed, which includes three main modules, is developed to perform the cellular activities generation, collection, and aggregation process between cell phones and cell stations. Then, the correlation between cellular data and traffic status data is studied. Finally, three scenarios using the data from testbed are demonstrated to measure the performance of the proposed method under different conditions. The results indicate that the proposed approach is a feasible and efficient way to simulate cellular data generation, collection, and aggregation process. Also, it can be the base for further analysis to detect traffic congestion on arterials using cellular data.
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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".