The effects of autonomous buses to vehicle scheduling system
Bibliographic record
Abstract
We are more and more close to the time when a higher number of autonomous vehicles are appearing in road traffic. The number of unanswered questions does not diminish but grow. One such issue is the role of autonomous vehicles in public transport. When talking about autonomous vehicles we often think of only cars and we think less about selfdriving buses. But the economic potential inherent in autonomous buses is huge. In the Hungarian vehicle and crew scheduling practice (and also in other countries) the one driver-one vehicle control is typical. This method closely links the vehicles and the drivers. Vehicles should therefore adapt to the rest time of the crew and the employment rules. Unused reserves are generated in the system. Autonomous vehicles can release this overcapacity. Thanks to that, fewer vehicles can carry out public transport tasks and we can save extra rides. It also provides a solution to the lack of drivers, which is a basic problem in many countries. In our study we show the reserves that can be recovered from the system in the case of three Hungarian cities (Eger, Dunaújváros, Győr). We show how much savings can be achieved by running autonomous buses in European cities with a population of 45 000, 54 000 and 130 000 inhabitants. The results are promising. In smaller cities we could achieve about 20% of economical savings but in bigger cities 40% is also realistic. Our statements are based on only rough calculations and they try to help in preparation for the future.
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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 | 0.000 | 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 | 0.000 |
| 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".