Optimization of Bus Depot Location with Consideration of Maintenance Center Availability
Bibliographic record
Abstract
Congested cities rely on public bus transportation services for a high share of its urban mobility. Unplanned disruptions to these services, their availability, and response time have significant impacts on passengers’ satisfaction. Optimally allocating buses to depots can undermine the impact of these disruptions as well as significantly reduce operational costs. Bus depots should be equipped with the necessary tools to serve and maintain the allocated buses. This study optimizes the assignment of buses to depots while taking into consideration the availability of maintenance resources. A mixed-integer linear program (MILP) formulation was developed to reduce the overall operational cost, optimize the assignment of buses to depots, and determine the optimum allocation of the maintenance centers. The model was validated by comparing computed results with those published in the literature for Vancouver Regional Transit System. Then, the model was further used to analyze another public transportation system. Results reveal that 17% savings in deadhead kilometers cost per bus can be achieved if limitations in the maintenance resources are considered at the planning stage.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".