Data‐Driven Driver Dispatching System with Allocation Constraints and Operational Risk Management for a Ride‐Sharing Platform
Bibliographic record
Abstract
ABSTRACT In this article, we develop and analyze a driver dispatching system for a control center that aims to minimize passengers' waiting time. The system imposes allocation constraints that ensure a minimum number of drivers in different regions to manage operational risk. The data‐driven system is based on Rolling Time Horizon approach and utilizes knowledge learned from historical data. It incorporates a hybrid forecasting model and a heuristic algorithm to solve the off‐line problem in each iteration. We show that the NP‐hardness of the off‐line problem lies in allocation constraints. We test the performance of the system with a simulation study based on actual past taxi order data. The result suggests that the system markedly decreases the average waiting time and saves planning time in comparison with the request‐driven dispatching mode. The result also demonstrates that in nonextreme cases, the dispatching system finds an acceptable solution which approximately satisfies allocation constraints while guaranteeing a short increase in waiting time.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".