Optimal vehicle capacity and dispatching policy considering crowding in public bus transit services
Bibliographic record
Abstract
This study investigates the often overlooked impact of on-board crowding on operational and user costs in public transit systems, specifically within a many-to-many bus transit line with varying demand patterns. While previous research has used mathematical programming for similar problems, this paper employs analytical approaches to offer deeper insights and address fundamental questions. First, we propose an approach to determine optimal bus capacities, factoring in in-vehicle crowding costs, assuming a fixed headway. Second, we explore the optimal dispatching policy for buses with fixed capacities, considering crowding costs. Third, we optimize both headway and vehicle capacity simultaneously. Our findings reveal that optimal vehicle capacity correlates with average passenger trip length and the square root of crowding-discomfort costs, especially when crowding increases linearly with load factor. When both headway and capacity are variable, smaller vehicles with shorter headways are favored, particularly in moderate-demand scenarios, especially with cost-effective staff models like autonomous fleets.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 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".