Eco-Drive Technology, Human Factors, and Environmental and Economic Benefits
Bibliographic record
Abstract
The objective of eco-drive technology is to reduce fuel consumption and resulting emissions using advances in communication and traffic control technologies with capability to support infrastructure-to-vehicle connection in a signalized network. On the human factors side, there is growing interest across the world in advising drivers to take eco-drive actions by effectively using the green phase of the signal cycle time to save fuel and reduce emissions. This paper describes a large-scale real-world research project in Ottawa (Ontario, Canada) on this subject. The technology and methods that support the green light optimized speed advisory (GLOSA) system were refined and all 1,178 traffic signals in the city were equipped to connect with a fleet of vehicles. Field study data were analyzed for speed trajectories, fuel consumption, and GLOSA compliance. Greenhouse emissions and fuel cost changes were computed. An anonymous questionnaire study investigated driver perception of the usefulness of the signal data displayed on an in-vehicle unit as advice on driving adjustment decisions made under prevailing traffic conditions. The over 65% compliance with GLOSA and the results of the driver questionnaire were mutually consistent. The fuel saving amounted to 7.6% but was adjusted to 5% because of uncertainties in daily vehicle travel. The reduction in carbon dioxide equivalent and fuel cost reported in the paper are based on a 5% adjustment. These results can be used for cost–benefit studies. Also, simulation-based research projects can verify their findings with the real-world experience reported in this paper.
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 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 | 0.001 | 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 | 0.001 |
| 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".