Optimizing fast charger location for hybrid electric bus transit networks
Bibliographic record
Abstract
The electrification of buses running on urban transit networks is one of the many weapons in the battle to limit greenhouse gas emissions. Existing diesel buses can be replaced by new fully electric buses or retrofitted to become hybrid. The latter is an interesting alternative in markets where electrification budgets are limited. Hybrid buses can run both on diesel and electric drive modes. They are typically equipped with low-capacity but fast-charging energy storage devices. As a result, their electric range is limited, but they can quickly charge en route while executing their tasks. In this paper, we devise a mixed integer programming model and two versions of a branch-and-check algorithm to locate chargers on multi-line hybrid bus transit networks. More specifically, our methods decide how many chargers to install at each candidate location and what should be the drive mode on each segment of each line in the network. The objective is to maximize the total distance driven using the electric mode. One novelty of our approaches is that they allow for charger sharing between lines. The latter allows for more cost-effective electrification of the network but makes the problem more difficult to solve as line service level and timetabling feasibility constraints become intertwined. We discuss extensive computational experiments on a set of 210 instances based on the transit network of the city of Tours (France). We provide managerial insights into the operational and economic benefits of allowing charger sharing and the trade-offs between increasing the budget and achieving greater electrification.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".