Analyzing Driver Compliance to Speed Limits Using Logistic Regression
Bibliographic record
Abstract
Driver compliance to speed limits is an important yet extremely complicated matter. The complexity arises primarily from the variety of factors that could affect drivers’ compliance to speed limits which could be vehicle, driver or road related, environmental, or even temporal. This study examines the effects of such factors on driver compliance in the City of Edmonton, using logistic regression. Unlike previous studies, this study examines the effects of different variables on compliance, rather than collision counts or driver speed choice. The dataset used includes vehicle spot speeds recorded at almost 700 different locations in the city. The compliance for each vehicle was used as the response variable for the regression model, which was built using data from more than 35 million cases. The findings show that, generally, the more restricted drivers become the more likely they are to comply with speed limits; potential restrictions include street parking, bike lanes, pedestrian crossing, or the absence of shoulder lanes. Furthermore, higher traffic activity during peak hours, and presumably on shoulder weekdays (Monday and Friday), both increase the likelihood of compliance. In contrast, as the vehicle class (length) increases, the probability of compliance decreases. Not much can be inferred about the effects of weather on compliance to speed limits, although an interesting finding is that odds of compliance seem to drop in winter months. Another important observation about non-compliance that is somewhat concerning is that speed limit violations are higher in residential areas relative to most of the other land uses considered
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".