Decision support tools for effective bus fleet electrification: Replacement factors and fleet size prediction
Bibliographic record
Abstract
• Ebus fleet size prediction models are developed for overnight depot charging. • Diesel-heated Ebuses require a lower replacement factor vs. battery-heated. • Winter conditions exacerbate fleet size requirements. • Total distance traveled and average temperature are key influencing factors. The electrification of public transit systems represents a crucial strategy for advancing sustainable urban mobility. Thus, the development of efficient charging infrastructure and the optimization of fleet size emerge as major challenges for transit agencies. Switching from diesel buses to electric buses (Ebuses) will require increasing the fleet size to accommodate the limited range of Ebuses and the significant idle time required for charging. This study develops prediction models to estimate the required Ebus fleet size to maintain same transit route services for the case of overnight depot charging, using data from Ebuses operating in the City of Toronto. The analysis reveals that Ebuses equipped with diesel auxiliary heaters are less sensitive to temperature fluctuations compared to battery-heated buses. Thus, the required replacement factor, indicating the additional fleet needed to switch from diesel to Ebuses, varies depending on the heating system. Specifically, diesel-heated buses require a lower replacement factor (1.3) compared to battery-heated buses (1.4), with winter conditions exacerbating this disparity. Furthermore, the study employs vehicular, operational, route, and external variables to develop the prediction models. Additionally, SHAP analysis is utilized to interpret the machine learning models and evaluate the influence of the inputs on the required fleet size. The results show that the total distance traveled, and the average temperature are the primary factors affecting the fleet size for Ebuses using their batteries for heating, whereas the total distance traveled, and the average bus speed are the primary factors affecting the fleet size for Ebuses with diesel auxiliary heaters.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".