Impacts of Holding Area Policies on Shared Autonomous Vehicle Operations
Bibliographic record
Abstract
Shared mobility has an important role in supporting existing transportation options in cities. However, when not deployed carefully, shared services may have operational inefficiencies such as low occupancies and increased deadheading. One reason is the spatio-temporal variance in the distribution of urban trip demand, which may lead to an unbalanced fleet displaced in cities thus unable to serve requested trips. Strategically siting holding areas (depots for dispatching and relocating fleets) could help improve fleet performance. Therefore, this paper considers shared autonomous vehicle (SAV) fleet operations by modeling the impacts of different holding area policies on service performance. Modeling and comparing multiple holding area policies for tactically deploying SAVs is novel, and the insights from this paper can inform service providers on how to site holding areas for improved performance. We develop a model of SAV fleet with pooling in the City of Toronto, with 27,951 total SAV trip requests across a 16-h period. We then integrate four holding area policies estimated using different spatial clustering methods, centralized positioning, and existing taxi stands. Findings indicate that using agglomerative clustering results in superior SAV fleet performances (average passenger waiting times reduced by about 20% compared with the worst performing policy), with increased served demand and reduced deadheading. A single holding area at a high trip density location yields efficient service performance at lower fleets but struggles to serve sparse demand (producing worst results); this method may suffice for operating SAV services within a small geofence with high trip densities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".