Impact Analysis Dashboard
Quantifying the effectiveness of mobility measures across Living Labs. This tool uses regression analysis to correlate the implementation of push/pull measures with changes in Key Performance Indicators (KPIs).
Domain-Specific Analysis
Filter impact data by specific domains such as Sustainability, Traffic Efficiency, or User Acceptance to isolate relevant trends.
Measure Attribution
Identify which specific policies (e.g., "New Bike Lanes", "Parking Restrictions") correlate most strongly with positive or negative KPI shifts.
Cross-Lab Comparison
Aggregated data from all participating cities provides a robust dataset for understanding the global impact of NSM adoption measures.
How to use this tool ?
3 simple steps to get started
How to use this tool ?
3 simple steps to get started
Select the domain of interest for the analysis
Choose from the list below (e.g., "Environment") to filter results by your area of interest.
View Ranked Measures
See which policy measures had the most significant positive or negative impact on the selected domain.
Analyze KPIs variations among Living Labs
Understand how different cities experienced changes in KPIs based on their specific combinations of measures, and explore the data through interactive visualizations.
Detailed methodology: Impact analysis methodology
Results where updated on 13 May 2026, 16:36
Measures linked to Environment improvement
Estimation of the strength of association for each measure to KPIs in the scope Environment.
Top 3 measures linked to Environment improvement
Measures statistically associated with KPI improvements
Streets retrofitting/introduction of priority lanes
Parking charges
Mobility hubs
Bottom 3 measures linked to Environment regression
Measures statistically associated with KPI decline
Introduction of new PT infrastructures/lines
Speed limits
Dedicated parking spaces for carsharing/micromobility
Comprehensive view of all 19 policy measures ranked by their association coefficient. Hover over bars to see detailed information and implementing cities.
Contribution levels by Policy measure
Click on the texts or bars to open detailsUnderstanding the Results
- Coefficients represent the estimated statistical association of each measure to KPI changes
- Positive values indicate measures most strongly associated with improvement of KPI values
- Negative values may indicate measures needing refinement or context-specific challenges
- Association strength from external conditions (out from policy measures analysed): -4.07
The associations reported by this assessment tool are algorithmic estimates derived from implemented measures and observed KPI changes. They indicate statistical associations, not proven causal relationships. Results may not exactly reflect real-world outcomes.
The associations reported by this assessment tool are algorithmic estimates derived from implemented measures and observed KPI changes. They indicate statistical associations, not proven causal relationships. Results may not exactly reflect real-world outcomes.