Towards smart transportation: A <scp>learning‐based data‐driven</scp> optimization approach for electric taxi dispatch problem
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
Electric taxi dispatch problem (ETDP) is one of the key issues in smart transportation. Existing study in the context of centralized optimization adopts either deterministic optimization, regular stochastic programming (SP) or simulation technique. Nevertheless, in data‐driven environment, the real passenger demands normally follow complicating probability distribution which cannot be described exactly by the parametric approaches. Hence, we propose a novel data‐driven optimization framework that integrates robust kernel density estimation (RKDE) and the two‐stage SP modeling technique. In particular, the probability distributions of customer demands are derived from historical data by RKDE, and the ETDP is formulated as a two‐stage SP model with the input parameters from RKDE. Meanwhile, a Monte Carlo method called sample average approximation is introduced to reformulate and solve the SP model. Finally, the experimental results show that the proposed approach outperforms the deterministic counterpart with the average demands as the input.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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