An Integrated Multicriteria Decision-Making Approach to Evaluate Traveler Modes’ Priority: An Application to Peshawar, Pakistan
Bibliographic record
Abstract
The transport planning is essential to meeting passengers’ needs for fast, safe, and reliable transport. The research goals of this study are to determine the most suitable mode of transport between predetermined alternatives according the criteria related to the transport planning. The research method combines GIS analysis, SWOT analysis, BEM method, and PROMETHEE II method in an integrated approach for decision-making. The methodology is applied to the city of Peshawar city. It includes six steps. First, a passenger questionnaire is used to establish passenger preferences when making a trip in the city. Secondly, alternative modes of urban transportation are defined. In the case of Peshawar, the following alternatives are considered: a new BRT service, BRT with five additional stops, old bus service, wagon, carpooling, and Careem/Uber. Thirdly, there is GIS analysis to investigate the stops of the BRT alternative transportation. GIS and satellite analysis have been completed for each stop. Fourthly, criteria for the assessment of urban transport modes are determined based on SWOT analysis. A total of twenty four subcriteria are proposed. Fifthly, the best-worst method (BWM) which is based on linear programming method is applied to determine the weightings that should be given to the main criteria and subcriteria. Sixthly, alternative modes of transportation are ranked by applying preference ranking organization method for enrichment evaluations’ (PROMETHEE II) method. The results show that the main important criteria greater than 5% are small movement interval: S4 (6%), security: S7 (13%), reliability: S8 (8%), accessibility:O1 (15%), possibility of special services: O2 (5%), possibility of including insurance in the travel tariff: O3 (8%), possibility of the modernization of the infrastructure: O4 (7%), and environmental pollution: T3 (5%). The implications of this study propose a BRT service with five additional stops is the best urban transport plan for Peshawar. The originality of this research consists in integration of a strategic planning technique SWOT analysis, GIS analysis, and multicriteria analysis in complete methodology to evaluate traveler’s modes priority. The methodology used in this research can be applied to evaluate different transport alternatives for transport networks worldwide.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".