Bibliographic record
Abstract
The safety at half-signalized intersections in Portland, Oregon is analyzed in this thesis using 10 years of crash history and analysis of video that was collected at a subset of intersections. A half-signalized intersection has a standard red-yellow-green traffic signal for automobiles on the major road, a stop sign for motorists on the minor road, and a pedestrian signal with actuation for pedestrians and/or bicyclists on the minor road. Although prevalent in Canada, this type of intersection control is not typically found in the United States because the MUTCD explicitly prohibits its use. Half-signal use is limited mostly to two cities in the Pacific Northwest. In Portland, Oregon there are forty-seven intersections where half-signals are used but the last installation was in 1986; Seattle has over 100 intersections with half-signals and installs these in new locations where warranted. To explore the safety records of these intersections in Portland, crash data from 2002-2011 was analyzed. A total of 442 crashes over the ten-year period at half-signals were observed. Sixteen of these 442 crashes involved pedestrians. In the crashes involving pedestrians, significant differences were found between the approach street of the vehicle and whether the pedestrian or driver was at fault. In the crash error reports, it was found that significantly more of the crashes involving pedestrians were the fault of motorists departing from the minor road who collided with pedestrians crossing the major street. Further crash analysis at half-signals was performed by developing matched comparison groups of minor stop controlled and fully signalized intersections. Crash rates were 0.158 and 0.178 crashes per million entering vehicles for 3-leg and 4-leg half-signals and these rates did not differ significantly from the minor street stop controlled and signalized comparison groups. Results from the matched comparison showed that the half-signalized group had more rear-end crashes when compared with the minor stop controlled group. This was the only result that held significance when crash rates were considered. It was also observed that the minor stop controlled group had a higher proportion of angle crashes when compared with the half-signal group but this did not influence the crash severity. Pedestrian crashes were more prevalent in the half-signal group when compared with the fully-signalized group. Pedestrian volumes were not available which would be used to determine if this significant measure is a result of higher pedestrian use at half-signals. In addition to crash analysis, video was captured at
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".