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 overnight 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. The formulation reflects the real-world selection and procurement process, which accounts for the interactions between transit agencies and tech-nology manufacturers or suppliers. In this work the proposed methodology is studied on a transit system composed of four short distance routes, to determine the final number of BEBs and chargers needed. The obtained results show the efficacy of the proposed approach.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".