The impacts of optimization approaches on BEB system configuration in transit
Bibliographic record
Abstract
Battery-electric buses (BEBs) are considered suitable technology for transit to tackle climate change and promote environmentally friendly mobility solutions. However, the systemic configuration of BEBs in transit requires sophisticated planning efforts due to contradictory objectives and decisions. The optimal design of a BEB transit system is often approached from various perspectives, leading to different system configurations and distinct impacts on the electricity grid. Towards that end, this study develops three BEB system configuration optimization models, including minimizing capital costs, electricity costs, and greenhouse gas (GHG) emissions. All three models inform the optimal charging system configuration, BEBs battery capacity, and BEBs charging schedule for a general hub-and-spoke transit network. The proposed models are applied to a case study of the Belleville City, Ontario, Canada, bus transit network. The results demonstrate that BEB system configuration and GHG emissions vary significantly according to the optimization perspective. Moreover, the findings emphasize the importance of using the energy storage system to reduce electricity costs and GHG emissions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".