Multi-Criteria Decision Analysis (MCDA) results for Quantitative Data (KPIs/policy measures)
Decision support using PROMETHEE-GAIA multi-criteria analysis, based on quantitative data such as KPIs and policy measures for sustainable shared mobility reported by living labs across Europe
Analysis Approach & Data Source
This analysis is based on KPI indicators, KPI groups, living-lab submissions, and policy measures reported by cities across Europe.
Analysis Approach & Data Source
This analysis is based on KPI indicators, KPI groups, living-lab submissions, and policy measures reported by cities across Europe.
66
KPI indicators included in this analysis
7
KPI groups used as MCDA criteria
11
Living labs contributing quantitative data
20
Policy measures evaluated as alternatives
KPI groups used as criteria in this run:
- Improve Safety
- Improve Public Transport
- Improve Accessibility
- Reduction of Congestion
- Reduction of Emission
- Improve Mobility Service
- Improve Multimodality
This quantitative MCDA result uses 66 KPI indicators grouped into 7 criteria, data from 11 living labs, and 20 policy measures evaluated as alternatives. Policy measures are scored against KPI groups through a ridge regression model that estimates the positive or negative contribution of policy measures to KPI changes observed across living labs. These estimated contributions form the input matrix for the PROMETHEE-GAIA analysis, which produces the final ranking of policy measures. Top-ranked policy measure: Ticketing integration in MaaS services. GAIA plane quality: 70%. Analysis completed on 30 Jul 2026, 15:41.
Public Transport Operators perspective
Results were updated on 30 Jul 2026, 15:41
Results
Review the analysis outcomes and recommendations
Top insights
Top performer
Highest overall score
Ticketing integration in MaaS services
φ = +0.236
Sensitivity level
Stability of top ranking
High sensitivity
Top alternatives are very close; small weight changes may swap ranks. Gap to #2: +0.052.
Conflict analysis
Most conflicting criteria pair
Improve Public Transport vs Reduction of Emission
Strong conflict detected in the GAIA plane.
Score spread
The difference between the highest and lowest overall score
+0.449
(max-min) = (+0.236) - (-0.212). How separated the alternatives are.
GAIA quality
2D projection representativeness
70.0%
Moderate confidence in GAIA interpretation.
Differentiating criteria
Most discriminating in the GAIA plane
Reduction of Congestion, Improve Public Transport, Improve Safety
Top 3 by vector length: 0.62, 0.59, 0.56.
The rankings and recommendations shown are derived from rigorous methodology and expert data, but they do not guarantee actual performance in your city or region. Factors such as local regulations, infrastructure, culture, and stakeholder engagement can all impact real-world results. Please use this information in conjunction with local expertise and detailed feasibility assessment.
The rankings and recommendations shown are derived from rigorous methodology and expert data, but they do not guarantee actual performance in your city or region. Factors such as local regulations, infrastructure, culture, and stakeholder engagement can all impact real-world results. Please use this information in conjunction with local expertise and detailed feasibility assessment.