Multiobjective eco-driving speed optimisation with real-time traffic: Balancing fuel, NOx, and travel time
Bibliographic record
Abstract
Optimising driving velocity profiles is crucial for reducing vehicle fuel consumption and NOx emissions without altering core vehicle components. While many studies have addressed eco-driving, most have focused solely on minimising fuel consumption or have treated NOx emissions separately, resulting in distinct, non-integrated speed profiles, and have often neglected the influence of real-time traffic. To overcome these limitations, this paper introduces a novel Multiobjective Speed Profile Optimisation (MO-SPO) framework for eco-driving that simultaneously minimises fuel consumption, NOx emissions, and travel time while accounting for surrounding traffic. Two solution approaches are developed and compared: a two-phase Model Predictive Control (MPC) method and a newly proposed Deep Reinforcement Learning (DRL) method that directly integrates multiple objectives and real-time traffic constraints into the speed control policy. Simulation results on a UK highway segment, with vehicle dynamics and engine characteristics derived from GT-SUITE data, demonstrate the benefits of the proposed framework. For instance, at one representative Pareto point, results indicate that the DRL approach achieves up to 10% lower fuel consumption and 16% lower NOx emissions compared to MPC-based methods while reducing travel time by approximately 5%. In addition, the DRL method maintained safer headway distances, offering more robust eco-driving strategies in dynamic traffic environments. This work is the first to apply multiobjective optimisation to generate integrated speed profiles that consider fuel, NOx, and travel time simultaneously under realistic traffic conditions.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".