Load Balancing for Mobility-on-Demand Systems
Bibliographic record
Abstract
In this paper we develop methods for maximizing the throughput of a mobility-on-demand urban transportation system.We consider a finite group of shared vehicles, located at a set of stations.Users arrive at the stations, pick-up vehicles, and drive (or are driven) to their destination station where they drop-off the vehicle.When some origins and destinations are more popular than others, the system will inevitably become out of balance: Vehicles will build up at some stations, and become depleted at others.We propose a robotic solution to this rebalancing problem that involves empty robotic vehicles autonomously driving between stations.We develop a rebalancing policy that minimizes the number of vehicles performing rebalancing trips.To do this, we utilize a fluid model for the customers and vehicles in the system.The model takes the form of a set of nonlinear time-delay differential equations.We then show that the optimal rebalancing policy can be found as the solution to a linear program.By analyzing the dynamical system model, we show that every station reaches an equilibrium in which there are excess vehicles and no waiting customers.We use this solution to develop a real-time rebalancing policy which can operate in highly variable environments.We verify policy performance in a simulated mobility-on-demand environment with stochastic features found in real-world urban transportation networks.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".