Method of Estimating the Number of Traffic Police Patrols and Standbys
Bibliographic record
Abstract
The purpose of the paper is to find a quantitative method that assists frontline traffic police departments with estimating the minimum number of police patrol and standby labor needed. Based on queuing theory and the analysis of the birth/death process, we develop and establish a two-dimensional M/M/c/m queuing model. Under the constraint of waiting queuing time required, we can obtain the minimum number of police patrol. In terms of the number of the standby traffic police, we take the cumulative distribution function (CDF) of emergencies into account and choose the 99th percentile minus the 95th percentile as the minimum number of the standby police. Combined with local traffic data, we use the method to measure and estimate the number of the frontline traffic police within the precinct of Beijing Capital International Airport (BCIA), which can optimize shift schedules to meet current performance benchmarks and prevent secondary accidents.
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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 | 0.001 | 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) | 0.001 | 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".