Optimization-based Control Algorithm: Development and Testing for Dynamic On-Demand SAV Operation
Bibliographic record
Abstract
Motivated by the growth of ride-sharing services and the technological evolution in autonomous vehicles (AV), this study seeks to develop and assess an operational platform for an on-demand autonomous vehicle hybrid sharing system (AVHS). The AVHS system is comprised of a fleet of AVs controlled by a Central Operation Manager (COM) that provides three levels of service to travelers ranging from a taxi-like service to a flexible-route, flexible-schedule transit-like system. The proposed AVHS system operational platform is built as a dynamic, sequential, and time-dependent stochastic control problem whose objective is to simultaneously minimize the costs associated with the operator and the traveller. This study develops a complex dynamic optimization-based control algorithm for a dynamic on-demand shared autonomous vehicle operation and tests the system's flexibility and resilience, through a sensitivity analysis on different testing cases defined by the AV fleet size, travel demand rate, and demand composition by levels of service. Results showed that the developed system was able to maintain the quality of service among different levels of services by reducing the travelers' waiting time, increasing vehicle occupancy, and reducing the number of empty vehicles miles traveled.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".