Perspectives on optimizing transport systems with supply-dependent demand
Bibliographic record
Abstract
The demand for transportation services often depends on what services are offered. Recognizing this dependency can enable transportation service providers to plan operations that are more profitable and sustainable. Yet little research on optimizing the planning of transportation systems explicitly recognizes that demand can be endogenous. We identify three challenges encountered when developing such methods. The first challenge is to develop models for demand prediction that are accurate, including for supply scenarios not observed in historical data. The second involves establishing how the accuracy of these demand models should be assessed in order to align with the downstream decision-making problem. The third involves formulating optimization models, and solution approaches for those models, that capture supply-demand interactions through an embedded demand model. For each challenge we present pointers to relevant research in different domains (econometrics, machine learning, operations research, and reinforcement learning) and identify future research directions. We ground the discussion of these challenges in a well-studied problem solved to plan freight transportation operations, the Scheduled Service Network Design Problem.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".