Data-Driven Optimization of Automated Speed Enforcement Logistics
Bibliographic record
Abstract
Canada’s collision fatalities are about 2000 lives a year, decreasing in the last decade, reaching 1745 in 2020 due to initiatives like Vision Zero. Among municipalities’ priorities is to enforce speed limits to reduce speeding-induced traffic fatalities, constituting 27 % of all traffic fatalities in Canada. An emerging strategy toward this goal is the deployment of , which detects violators through speed cameras positioned alongside designated roads. Empirical evidence from existing Automated Speed Enforcement (ASE) practices shows that the number of citations drops each month as driver become aware of camera locations and lower their driving speeds. Hence, ASE cameras are often relocated in cycles to expand their reach to more places and further deter speeding violations. The complexities of deployment lie in choosing camera locations and cycle duration, which have the highest deterrence impact on speeding during a planning period. This study proposes a data-driven model to classify camera site locations based on the effectiveness of ASE enforcement. Then, a Markov decision process optimization model is presented to find the optimal camera locations at each cycle and the length of the cycles for minimizing speed violations across the entire transportation network, considering limitations such as the number of available cameras.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".