Dynamic Eco-Driving on Signalized Arterial Corridors during the Green Phase for the Connected Vehicles
Bibliographic record
Abstract
Inappropriate driving behaviours can result in additional fuel consumption and emissions. Drivers can be informed of the accurate signal phase and timing (SPaT) and distance information of the current intersection and downstream intersections via vehicle-to-everything (V2X) communications. The real-time information has been utilized to assist drivers in taking reasonable manoeuvres and gain lots of benefits on fuel consumption and emissions in some existing studies. In order to cooperatively address the optimization problem on the signalized arterial corridors, this paper presents an eco-driving optimization model considering preceding SPaT and position information. This model can be applied to pass two successive traffic signals cooperatively during green phase. In this study, a multi-stage optimal approach is proposed to minimize the fuel consumption. Field experiments are carried out for comparative analysis between the connected vehicle with speed advisory and the uninformed vehicle without speed advisory. The results indicate that the fuel saving of the connected vehicle guided by the dynamic optimization algorithm shows significant improvement. In addition, the rolling optimization among three signalized intersections is conducted and the results show that a considerable improvement can be obtained compared with the one-by-one optimization.
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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".