Comprehensive Fleet and Charger Sizing for Public Transportation Electrification Considering Route Assignment
Bibliographic record
Abstract
Public transportation electrification is a topic of great interest due to its potentially significant impact on the reduction of greenhouse gas emissions. In order to electrify the public transportation system, the first stage is to determine the appropriate sizing of the necessary assets. Consequently, the goal of this work is the sizing of the fleet and chargers for transit agencies that choose to operate their fleets using two different modes of charging: overnight and opportunity charging. The developed methodology incorporates detailed route assignment, energy consumption modeling, and charging requirements for electric fleets. The problem goes through several stages: day-time operation is first modeled for every route individually to determine battery electric bus (BEB) route assignment while enforcing battery state of charge (SOC) constraints. Next, night-time operation is modeled to determine the optimal number of chargers needed to fully charge the fleet in preparation for the next day's operation. Once the operational formulation is completed, the planning formulation which determines the final selection of the assets to be purchased is presented. This formulation reflects the real-world selection and procurement process, which accounts for the interactions between transit agencies and technology manufacturers or suppliers. In this work the proposed methodology is applied on a transit system comprised of four short-distance Canadian routes, to determine the final number of BEBs and chargers needed for both modes of charging. The results highlight the efficacy of the proposed approach in determining the operation of the fleet as well as the required number of chargers required.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".