Before and after traffic safety evaluations using computer vision techniques
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Traditionally, road safety analysis has been undertaken using historical collision records. This approach to road safety analysis is reactive in that the analyst has to wait for collisions to take place before an action can be taken. An alternative approach is to study traffic conflicts or near misses which occur more frequently, can be clearly observed and are related to collisions. However, there are issues of subjectivity, reliability, and cost associated with the use of human observers. The use of computer vision techniques to automate the process of collecting traffic conflicts data can help mitigate these problems. This thesis presents the results of a before-after safety evaluation of a proposed design for channelized right-turn lanes. The evaluation uses an automated safety analysis approach to identify and measure the severity of traffic conflicts. The new design, termed “Smart Channels”, decreases the angle of the channelized right turn to approximately 70 degrees, and is considered to have safety benefits for both vehicle-pedestrian and vehicle-vehicle interaction. Data for three treatment sites and one control site, located in British Columbia, Canada, are evaluated using automated traffic conflict analysis that relies on computer vision for conflict detection. The results of the evaluation show that the implementation of the right-turn treatment has resulted in a considerable reduction in the severity and frequency of merging, rear-end, and total conflicts. The total average hourly conflict was reduced by a statistically significant 51 percent, while the average conflict severity was reduced by a statistically significant 41 percent. Many different traffic conflict indicators have been proposed and studied, but the methods of combining the results has not been well examined. This thesis considers four conflict indicators and examines methods of combining or aggregating the information provided by each indicator in order to better account for all components of risk in traffic conflicts. The four indicators are time-to-collision, gap-time, deceleration-to-safety time, and post-encroachment time. Two primary aggregation methods are studied: time aggregation and road-user aggregation. Time aggregation is appropriate for determining aggregate severity over periods of time, and road-user aggregation is used for normalizing risk to the volume of users.
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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 | 0.000 | 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) | 0.000 | 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