Data-Efficient Vehicular Fuel Rate Estimation Via Transfer Learning Using a Nonlinear-Autoregressive-Exogenous Neural Network
Bibliographic record
Abstract
Fuel estimation is crucial for intelligent transportation systems to minimize fuel consumption, thereby reducing costs and environmental impacts. However, developing accurate data-driven fuel-estimation models requires extensive data, which is usually costly and time-consuming to collect for feet systems with a large number of vehicles. This paper presents an inductive transfer learning (TL) method for fuel rate estimation to alleviate the large fuel data measurement requirement. First, a dynamic neural network based on the Nonlinear Auto-Regressive Exogenous (NARX) approach is trained for a Peterbilt 579 class 8 haul truck as the base model, using on-road fuel consumption measured data. Afterward, using the TL method, the developed fuel-estimation model is leveraged to three different vehicles with various engine sizes and types, including a Ford F-350, a hybrid Ford Fusion, and a turbocharged Ford Escape. Extensive analyses illustrated that the transferred target models require training data as minimal as 3% (3km) of the total data used for training the base model to achieve prediction accuracy comparable to the base model. Moreover, compared to developed conventional models, the TL-based target models for the Ford Fusion, Ford Escape, and Ford F-350 demonstrate average improvements of 70%, 71%, and 73% in terms of Mean Absolute Error (MAE), Rooted Mean Squared Error (RMSE), and training time, respectively.
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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 | 0.000 | 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".