Driver guidance and rebalancing in ride-hailing systems through mixture density networks and stochastic programming
Bibliographic record
Abstract
We propose a data-driven optimization model to reduce riders' wait time for vehicle guidance and rebalancing operations, considering the rider demands are under uncertainty. Instead of assuming a pre-defined rider demand distribution, we propose a data-driven framework that integrates Mixture Density Networks (MDNs) and a two-stage stochastic programming model. The integrated framework can compute high-quality guidance and rebalancing solutions that benefit drivers and riders in the ride-hailing system by leveraging the time-series historical data from real data sets. To prove the performance and effectiveness of our approach, we conduct a group of simulations based on the New York High Volume For-Hire Vehicle (HVFHV) trip records. The validation results show that the proposed method outperforms the data-driven deterministic models using GRU and moving average methods. Most significantly, the riders' average wait time using our proposed approach can be reduced by 75.9% compared to the batched matching mechanism.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 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".