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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 31 Jul 2026, 11:21

2

Measures linked to Modal Split - Sustainable private modes improvement

Estimation of the strength of association for each measure to KPIs in the scope Modal Split - Sustainable private modes.

Moderate evidence(7 cities)
Policy Measures statistically associated with KPI improvements
9
Policy Measures statistically associated with KPI decline
10
Total Policy Measures considered
19
Cities with data
7
Total KPIs metrics compared
6
Model Quality (MSQE)
1.67e+0
Top 3 measures linked to Modal Split - Sustainable private modes improvement

Measures statistically associated with KPI improvements

1
Improved information about the availability of shared modes
+3.11strength of association with KPIs improvement3 cities implemented
2
Parking charges
+2.90strength of association with KPIs improvement4 cities implemented
3
On demand vehicle sharing action plan
+2.29strength of association with KPIs improvement4 cities implemented
Bottom 3 measures linked to Modal Split - Sustainable private modes regression

Measures statistically associated with KPI decline

17
Introduction of new PT infrastructures/lines
-2.44strength of association with KPIs decline2 cities implemented
18
Introduction of new NSM services
-2.40strength of association with KPIs decline5 cities implemented
19
Limited traffic zone/Pedestrianisation of streets
-2.28strength of association with KPIs decline6 cities implemented

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 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): +2.37
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.