FECO: An Efficient Deep Reinforcement Learning-Based Fuel-Economic Traffic Signal Control Scheme
Bibliographic record
Abstract
Vehicle fuel efficiency (VFE) has a pivotal role in solving energy shortage issue due to the increasing global demand for energy. The high frequency of go-stop movements and long waiting times at intersections significantly reduce the VFE. Such negative impacts are particularly severe when the traffic flows are regulated by poorly designed traffic signal control. Existing works have successfully applied deep reinforcement learning (DRL) techniques to improve the efficiency of traffic signal control. However, to the best of our knowledge, few studies have explored traffic signal control for VFE through eco-driving techniques. To fill the gap, we propose a DRL-based fuel-economic traffic signal control for improving vehicle fuel efficiency. Briefly, we adopt the DRL-technique to develop an agent that can efficiently control traffic signals based on real-time traffic information at intersections, and adjust speed profiles for approaching vehicles to smooth traffic flows. We tested our method on both synthetic traffic dataset and real-world traffic dataset from surveillance cameras in Toronto. Through comprehensive experiments, we demonstrate that our method surpassed the performance of both pure eco-driving and pure traffic signal control techniques by significantly reducing vehicle fuel consumption and improving the efficiency of traffic signal control.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".