Energy-Efficient Route Navigation (Eco-Routing) for Electric Vehicles in SUMO
Bibliographic record
Abstract
The diffusion of electric vehicles (EVs) is recently gaining great attention in the road transport and automotive sectors as an attempt to bring in an emission-free world. EVs are considered a key to future clean transportation systems. However, these vehicles still suffer from limited battery capacity and range anxiety. Therefore, EVs manufacturers are focusing on reducing energy consumption and CO2 emissions. In addition, research in the context of intelligent transportation systems embedding information and communication technologies are focusing on the optimization of the energy consumption as a valuable solution to foster the wide diffusion of EVs. In this article, we propose a simulation platform for eco-routing services based on estimating EV energy consumption to provide the most energy-efficient routes for the EV while traveling. We provide an energy map that can be used for eco-routing through a real-time data collection of the EV energy consumption. The energy map was established in the traffic simulator Simulation of Urban MObility (SUMO) to show the efficiency of the proposed eco-routing strategy compared to the other strategies based on establishing the fastest routes. This map will be exploited as good support, in the future, for advanced research on the EV concept.
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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 | 0.001 | 0.001 |
| 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".