Impact of Electrification on the Required Bus Fleet Size: The Case of Overnight Depot Charging
Bibliographic record
Abstract
The electrification of public transit systems, particularly the adoption of electric buses, stands as a critical strategy in the global pursuit of sustainable urban mobility. Amidst this transition, the establishment of robust charging infrastructure and the optimization of fleet size emerge as pivotal challenges for transit agencies and policymakers. This paper delves into the specific implications of overnight depot charging on the required bus fleet size, focusing on the Canadian context. By analyzing operational data from electric bus deployments in Toronto, the study investigates the impact of limited electric bus range on the fleet size requirements. The results reveal that, on average, a 34% increase in fleet size is necessary to maintain equivalent service levels compared to traditional diesel buses. In addition, the results show that the required replacement factors vary across the different seasons, indicating variable fleet size requirements based on energy consumption rates. Moreover, bus type also influences fleet size requirements, with buses featuring higher battery capacities exhibiting lower replacement factors. This study underscores the necessity of understanding these dynamics for effective decision-making in electric bus deployment. Insights derived from this research offer valuable guidance for transit agencies, policymakers, and stakeholders involved in advancing sustainable urban transportation initiatives.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.005 | 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".