Integrating Frequency Setting, Timetabling, and Route Assignment to Synchronize Transit Lines
Bibliographic record
Abstract
Synchronization of different transit lines is an important activity to increase the level of service in transportation systems. In particular, for passengers, transferring from one line to another, there may be low-frequency periods and transfer zones where walking is needed, or passengers are exposed to adverse weather conditions and uncomfortable infrastructure. In this study, we define the Bus Lines Synchronization Problem that determines the frequency for each line (regarding the even headway), the timetable (including holding times for buses at transfer stops), and passenger-route assignments to minimize the sum of passenger and operational costs. We propose a nonlinear mixed integer formulation with time-indexed variables which allow representing the route choice for passengers and different types of costs. We implement an iterative heuristic algorithm based on fixing variables and solving a simplified formulation with a commercial solver. We implement our proposed heuristic on the transit network in Santiago, Chile. Numerical results indicate that our approach is capable of reducing operating costs and increasing the level of service for large scenarios.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".