Transit electrification through charger deployment, fleet type selection, and charging schedule optimization
Bibliographic record
Abstract
Transit electrification requires strategic planning of chargers, fleet selection, and charging schedules. This study presents an integrated optimization model considering these elements alongside operational factors, such as time-of-day electricity prices, enabling realistic assessment of charging costs. We address the computational complexity of electrifying large-scale transit networks through a heuristic algorithm that decomposes the problem into two sub-problems, where the first determines the optimal charger locations, and the second optimizes fleet selection and allocation to bus blocks and charging schedules. We extract network and operational characteristics, like layover times, from GTFS data. The heuristic algorithm produces solutions within 2.2% of the optimal on average for medium-sized networks, demonstrating its effectiveness in finding near-optimal solutions. Our analysis shows that although the charging rate affects the number and capacity of chargers, it has a lower effect on the distribution of battery types. The results reveal that higher electricity prices incentivize the adoption of larger battery capacities, as they enable buses to shift charging to lower-cost periods and reduce reliance on expensive peak-hour electricity. We examine the transit networks of 20 cities and districts and find that in Washington, Philadelphia, and Brooklyn, more than 70% of buses can be electrified solely with overnight charging without requiring recharging during the day. In contrast, Los Angeles, Houston, and Calgary require substantial en-route charging to achieve high electrification while completing routes without running out of charge. • A joint optimization model for charger siting, fleet type, and scheduling is presented. • A scalable two-stage heuristic for large urban transit networks is introduced. • Validates the heuristic against exact solvers with <3% deviation and faster runtime. • Applies the framework to 20+ North American cities. • Conducts sensitivity analysis on energy consumption rate, charging rate, and electricity price.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".