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Analysis Tool

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.

Learn how it works Start your analysis

How to use this tool ?

3 simple steps to get started

1

Select the domain of interest for the analysis

Choose from the list below (e.g., "Environment") to filter results by your area of interest.

2

View Ranked Measures

See which policy measures had the most significant positive or negative impact on the selected domain.

3

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

2

Measures linked to Safety & comfort improvement

Estimation of the strength of association for each measure to KPIs in the scope Safety & comfort.

Moderate evidence(7 cities)
Policy Measures statistically associated with KPI improvements
5
Policy Measures statistically associated with KPI decline
12
Total Policy Measures considered
17
Cities with data
7
Total KPIs metrics compared
10
Model Quality (MSQE)
1.06e-1
Top 3 measures linked to Safety & comfort improvement

Measures statistically associated with KPI improvements

1
Streets retrofitting/introduction of priority lanes
+0.66strength of association with KPIs improvement3 cities implemented
2
Central PT planning and construction program
+0.64strength of association with KPIs improvement4 cities implemented
3
Limited traffic zone/Pedestrianisation of streets
+0.42strength of association with KPIs improvement5 cities implemented
Bottom 3 measures linked to Safety & comfort regression

Measures statistically associated with KPI decline

15
Mobility hubs
-0.78strength of association with KPIs decline5 cities implemented
16
Improved NSM infrastructure, e.g. bike lanes & "park & ride"
-0.56strength of association with KPIs decline6 cities implemented
17
Introduction of new PT infrastructures/lines
-0.45strength of association with KPIs decline3 cities implemented

Comprehensive view of all 17 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 details
#Number of living labs implementing the policy measure.
Understanding 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): +1.15
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.