Hub Network Design for Strategic Autonomous Shuttle Deployment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Integrating shuttles into an urban transit system can play a pivotal role in creating safer, sustainable, accessible, affordable, and less congested urban environments. This paper studies the design of hub networks for strategic deployment of autonomous shuttles. Given a set of passenger trips in an urban area, the problem is to determine the origins and destinations of a fixed number of hub arcs that represent shuttle connections to maximize the potential users of the system. The problem is formulated as a maximal covering hub arc location model and solved to optimality using Benders decomposition. Several algorithmic enhancements, including using reduction tests to eliminate variables and adding multiple Pareto-optimal cuts, are proposed to improve the convergence of the Benders decomposition algorithm. Additionally, two data-driven clustering-based methodologies are adapted and implemented to compare and validate the solutions of the optimization model. All methodologies are tested using the New York City taxi trip data. Several computational experiments are conducted to compare optimization and data-driven approaches under key performance metrics that include the total number of commuters that use the shuttle system, the percentage of satisfied trips by these shuttles, the utilization of the shuttles, and the driving and walking distances. The results from the optimization model yield more satisfied trips through the system and also a more balanced utilization of the shuttles compared with the results obtained from either of the clustering-based methodologies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 it