Evaluating the impacts of changeable message signs on traffic diversion
Bibliographic record
Abstract
The Ontario Ministry of Transportation has 17 CMS installed strategically upstream of Express-Collector transfer locations on Highway 401 in Toronto, Canada. Motorists are informed by the CMS of traffic conditions downstream of the transfer location to help them decide whether to take the next transfer. Loop detectors are installed at the transfer locations to measure traffic flow. This paper evaluates the impact of CMS messages on traffic diversion using 3 years of loop detector data from 2003 to 2005. We have found that on average a CMS message change can alter the diversion rate by up to around 5%, and can shift up to around 300 vph. These numbers depend strongly on the specific initial and final messages, and on the location of the CMS. Drivers' reactions to CMS messages tend to be higher in the afternoon than in the morning. The impacts of CMS messages on drivers appear to be diminishing over the years
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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 | 0.000 | 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 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".