Data Driven Synchronization Strategies of a Bus Line in a Transit Network
Bibliographic record
Abstract
The waiting time of passengers at transfer stations is one of the most important criteria to measure the service quality of public transportation. Because of the stochastic nature of traffic, scheduled transfers cannot always occur. This research proposes an online control framework for a bus line using holding, skip-stop and speed change tactics. We build an arc-flow optimization model enumerating all possible tactics within a time horizon. The model minimizes total passenger travel times by improving, among others, transfer times and reducing deviations from the bus schedule. Decisions are based on real-time passenger flow data and travel times. The methodology was tested on a case study of the bus system of the city of Laval, Canada. A simulation framework has been developed, integrating data on smart card transactions and bus locations, to verify the performance and results of the optimization model. Data generation in the simulation framework is improved using a training set. Different levels of uncertainty are introduced on instances of a testing set and the resulting optimal parameters are applied to a validation set.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.003 |
| 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".