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 Dashboard
3 simple steps to get started
How to Use This Dashboard
3 simple steps to get started
Select a KPI Domain
Choose from the dropdown below (e.g., "Sustainability") to filter results by your area of interest.
View Ranked Measures
See which policies had the most significant positive or negative impact on the selected domain.
Analyze Coefficients
Understand the magnitude and direction of each measure's impact through detailed regression coefficients.
Methodology & Technical Details
Regression analysis approach
Methodology & Technical Details
Regression analysis approach
This analysis employs regression modeling to estimate the contribution of individual measures to KPI changes. By comparing pre- and post-implementation data, we calculate coefficients that represent the magnitude and direction of each measure's impact.
The coefficients shown are derived from a multiple regression analysis where each measure is treated as an independent variable, and the KPI change is the dependent variable. Positive coefficients indicate measures that contributed to KPI improvement, while negative values suggest adverse effects or areas requiring attention.
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