Decision-Support Framework for Selecting the Optimal Road Toll Collection System
Bibliographic record
Abstract
One of the central decision-making questions in planning road tolling is the selection of the optimal toll collection system (TCS). The question of TCS selection arises in the situation when the existing TCS is to be upgraded or when TCS is selected for a newly constructed road. Considering that there are multiple TCS available nowadays, with their particular advantages and disadvantages, and that there is a range of often conflicting criteria for TCS selection, this decision-making issue belongs to the group of multicriteria decision-making (MCDM) problems. The MCDM-based methodology used in this research integrates Strengths-Weakness-Opportunities-Threat (SWOT) analysis and Fuzzy Preference Ranking Organization Method for Enrichment Evaluations (F-PROMETHEE). The expert-based decision-support framework includes a procedure for defining evaluation criteria and their weights, scoring of alternatives, and sensitivity analysis. Presented decision-support framework is tested with fourteen toll systems. Results indicate that the best-ranked TCS is the dedicated short-range communication multilane free flow. Decision-support framework is developed for transferability to different contexts, where local features can be taken into account by choosing specific alternatives, criteria, and criteria values. Finally, this development opens up opportunities for further analysis of criteria values and considerations of user attitudes in road pricing scheme planning.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".