City of Ottawa-I2V Connected Vehicle Pilot Project – City Fleet-Signalized Intersection Approach and Departure Optimization Application
Bibliographic record
Abstract
The City of Ottawa EcoDrive II project investigated the potential environmental and fuel efficiency benefits of providing drivers with advanced signal information using Green-light Optimized Speed Advisory (GLOSA) technology. GLOSA uses traffic signal information and the current position of a vehicle to display a speed recommendation on mobile app. The recommended speed is the travel speed a driver should maintain to pass through an upcoming signalized intersection during the green phase. Reducing stops at red lights can help reduce fuel consumption and emissions and improve traffic efficiency. The report presents the results and analysis of data collected during a two-month period across the city’s 1,200 traffic signal system. The detailed analysis conducted by Carleton University provides the evaluation of the project and potential benefit of GLOSA Infrastructure-to-Vehicle (I2V) technology when applied to connected vehicles in a city fleet application. The study also examined key factors that influenced fuel consumption, including: - acceptance of the technology and the willingness of the driver to adjust their driving habits; - existing traffic volume during data collection; and, - road classification of the testing route.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".