Bibliographic record
Abstract
This paper discusses how information using digital figures about the seconds left until the changing of the signal indication to RED is wide spread all over the world. Surprisingly their effects on the acceptance of the signal control are less researched. Many examples in China, Japan, Denmark, Ireland and Turkey have been realized without any research about their effectiveness. Furthermore there are a lot of installations in the United States and Canada that provide pedestrians with the remaining seconds available before the pedestrian phase ends; and in many cases they show only the time remaining to cross. This is a great difference to the Hamburg experiment. Hamburg in the north of Germany and is the second largest city (1.7 million inhabitants) of the country and is ranked eighth in the worlds harbor chart (7 million TEU per year). In 2005 the local government voted for a pilot project to provide countdown signals for the first time in Germany. In the first step a pedestrian crossing in the Central Business District (CBD) was equipped with countdown RED signals for pedestrians. As a simplified test of effectiveness a before-and-after-study was carried out covering the following aspects: traffic volume of cars and pedestrians, delay and acceptance of the signals. The two pillar approach consists of a traffic engineering survey and roadside interviews and this is to make sure that data from two levels (objective by traffic counts and subjective by the questionnaires) interpret the results. Due to the activities in the CBD (shopping, entertainment, work places) the research intervals were fixed to 11 a.m. – 1 p.m., 4-6 p.m., and 8-10 p.m. Altogether 45,000 cars and 71,000 pedestrians crossing the street had been recorded and 760 questionnaires had been evaluated. Behavior in transport is embedded in the behavior patterns of other social sectors. That is why red-light offenses by pedestrians and cyclists are widespread in Germany. Countdown signals have a significant influence on this misbehavior. As a whole (for both directions and all intervals) the red light-running share dropped from 21.0% to 16.7% which means a reduction of 20%. This result is very remarkable.
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How this classification was reachedexpand
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".