Predictive Eco-Routing with Mixed Powertrains Under Connected Environment
Bibliographic record
Abstract
Eco-routing enables individual vehicles to identify the most energy-efficient routes. Routing strategies, however, might be different between powertrain technologies given their different energy consumption behaviors. Such heterogeneity, coupled with the unpredictability in traffic dynamics due to non-recurring events (e.g. road incidents), poses substantial challenges to effectively implement eco-routing and quantify its energy implications. This paper proposes a novel predictive eco-routing system that leverages the capabilities of connected vehicles (CVs) to deliver optimal routing solutions with predicted traffic information for different powertrain technologies. By integrating power-based energy models with a deep neural network, the system can accurately estimate and predict link-level traffic conditions and energy usage. The proposed system is tested via microscopic simulation and demonstrated to reduce both energy consumption and travel delays for each powertrain technology, with internal combustion engine vehicles (ICEV) and hydrogen fuel cell vehicles (HFCV) achieving more energy reduction than electric vehicles. The results also highlight that predictive routing outperforms real-time routing, especially in networks with non-recurring events like road incidents. The sensitivity analysis verifies the reliability of the proposed system in supporting eco-routing at different congestion levels.
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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 | 0.000 | 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 | 0.000 | 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".