Autonomous Bus Operation Alternatives in Urban Areas Using Fuzzy Dombi-Bonferroni Operator Based Decision Making Model
Bibliographic record
Abstract
Advances in V2V, V2I, and autonomous vehicle technologies have made autonomous buses possible in cities. Soon, autonomous bus operations will be common in urban areas, which will improve sustainability, safety, and the city’s technology. These buses have different operation types. Each operation has advantages and disadvantages. Therefore, the goal of this study is to serve as a guide for decision-makers during the transition to autonomous buses. Four alternatives are evaluated based on eleven criteria organized under four main aspects, namely autonomous buses for special uses, autonomous buses for last-mile uses, autonomous cars in mixed traffic, and autonomous buses in closed systems. We propose an Ordinal Priority Approach (OPA) method for determining the criteria weights and application of fuzzy Dombi Bonferroni (DOBI) methodology for the evaluation of alternatives. When compared to the other three alternatives in this study, the results show that deploying autonomous buses in mixed traffic is the most advantageous option.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".