Investigating trade‐offs between optimal mobile photo enforcement programme plans
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Agencies that manage mobile photo enforcement (MPE) programmes must decide where and when to send their limited resources to monitor compliance with speed limits. Usually, the goal is to select locations based on a number of concerns (i.e., high collision sites, high speed violation sites, school zones, etc.), which, in most cases, is conflicting. If certain locations are given more MPE resources, then by definition, other locations will receive less attention, and vice versa. This paper aims to provide insights about such MPE programme trade‐offs. We present a systematic procedure for interpreting the results of a multiobjective MPE resource allocation problem. The procedure consists of three steps: (a) Pareto front (PF) generation, (b) front representation, and (c) trade‐off analysis. First, in generating a PF, we sequentially apply two well‐known scalar optimization methods to obtain a comprehensive set of Pareto‐optimal solutions. Second, the K ‐medoids clustering algorithm and the silhouette index are adopted to partition the generated PF into similar‐sized clusters, in order to help MPE programme agencies choose from a reduced set of solutions on the PF. Third, we use the response surface method to determine trade‐off patterns on the PF. The results of the front generation analysis showed that applying two optimization methods together resulted in a nearly complete PF with a relatively uniform and dense spread of solutions. Consequently, the identified set of solutions (i.e., 13,210 cases) was further partitioned into 12 clusters by silhouette index and K ‐medoids. With the aim of reducing decision fatigue for agencies, each cluster's representative solution is considered a possible MPE resource allocation candidate. The trade‐off analysis indicated how much one must sacrifice in the other objectives in order to increase attainment of one particular objective. Finally, the trade‐off rate and elasticity were used to explore the quantitative relationship between the considered objectives.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it