Strategic Deployment of Electric Buses Through Replacement Factor Prediction: A Machine Learning Framework for Cost‐Effective Electrification
Bibliographic record
Abstract
ABSTRACT The transition to electric buses (e‐buses) is essential for reducing greenhouse gas emissions in urban transit systems. However, successful e‐bus deployment requires careful planning to ensure service reliability while minimising costs. A key challenge in this transition is determining the replacement factor, the ratio of e‐buses needed to replace the current diesel‐engine bus fleet for a certain route. This factor is essential for transit agencies as it directly influences fleet size, capital investment, and operational efficiency. Accurately estimating replacement factors allows agencies, to prioritise routes where electrification achieves the highest economic and environmental benefits while preventing unnecessary fleet expansion and idle capacity by selecting routes with low replacement factors. This study develops a framework for estimating e‐bus replacement factors based on route characteristics, vehicle attributes, and external conditions. Multiple machine learning models are evaluated, with XGBoost achieving the highest accuracy (R 2 = 0.93). Model interpretability using SHapley Additive exPlanations (SHAP) analysis identifies the average bus speed and ambient temperature as the main variables affecting the replacement factor. The proposed framework enables transit agencies to optimise fleet deployment by prioritising routes with lower replacement factors, maximising e‐bus utilisation, and achieving cost efficiencies while aligning with environmental objectives.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".