Bibliographic record
Abstract
Speeding is a leading cause of urban collisions and often causes injury and death. Consequently, photo enforcement has been globally adopted as a countermeasure in speed management and has been proved to be effective in mitigating speeding problems. Although there is extensive research pertaining to photo enforcement, there is a critical research gap regarding the development of an integrated deployment, scheduling, and evaluation process for Mobile Photo Radar Enforcement (MPRE). As a result, the objective of this thesis is to develop a framework for the MPRE program, aiming to provide planners and schedulers with a systematic and analysis-based procedure to design the MPRE program and schedule enforcement activities. The thesis used the City of Edmonton’s (CoE) current MPRE program as the basis to showcase the proposed framework. The literature relating to the theoretical basis of enforcement, the assessment of MPRE’s effects, and the deployment strategies of MPRE used in other jurisdictions was reviewed; information about the current MPRE program in the CoE was consolidated; and the historical data were collected and analyzed. Based on the collected information, a program framework was proposed, which factored in local program needs and institutional characteristics. The framework consists of three parts: a multi-variable site identification and priority-base site selection process for screening potential MPRE locations, the scheduling method for deployment of MPRE, and guidelines for program evaluation and adjustment. The framework was illustrated and tested using a numerical example. The performances of different scheduling methods proposed in the thesis were compared. Although the program framework is designed for the CoE, the systematic procedure and methodologies can be applied to MPRE programs in other jurisdictions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".