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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.

Understanding the methodology

Regression analysis approach

The impact analysis assesses the effect of push and pull measures β€” combinations of restrictions on private car use (push) and incentives for shared mobility adoption (pull) β€” implemented across multiple Living Labs as part of Sustainable Urban Mobility Plan (SUMP) interventions.

The analysis follows a three-step process:

  1. Baseline data collection : KPIs are measured before the interventions.
  2. Impact estimation : KPIs are recalculated after implementation and compared to the baseline.
  3. Interactive interface : Results are communicated through a dedicated interface to support stakeholder exploration.

To quantify the contribution of each measure, a Ridge regression model (linear regression with L2 regularization) is used. Each measure is encoded as a binary variable (implemented = 1, not implemented = 0) per Living Lab. The model associates a regression coefficient to each measure, estimating its individual contribution to the observed change in each KPI. Ridge regularization is chosen because the number of measures exceeds the number of Living Labs, helping prevent overfitting and address multicollinearity.

Both univariate (per KPI) and multivariate (grouped KPIs of the same type, ie. environmental, societal, economic) analyses are conducted to ensure robustness, especially when data points are limited for local KPIs.

Data collection

What data is this analysis based on?

KPIs tracking

Collection of KPI data before and after the implementation of push and pull measures across all Living Labs

Push and pull measures implementation

Binary records (0/1) per Living Lab indicating which push and/or pull measures were applied (e.g. parking charging, vehicle sharing)

9 Living Labs

Cities participating in the SUM Open Data Platform that have implemented various combinations of push and pull measures, providing a diverse dataset for analysis

The analysis draws on data collected across all Living Labs, combining intervention records and performance measurements (KPIs). These inputs feed into the regression model to estimate the impact of each measure on mobility outcomes.

How to interpret the results ?

Understanding regression coefficients and model accuracy

The model outputs a regression coefficient (Ξ²) for each measure, representing its estimated contribution to the observed change in a given KPI:

  • A positive coefficient indicates the measure contributed to an increase in that KPI.
  • A negative coefficient indicates a decrease.
  • A coefficient close to zero suggests the measure had little to no isolated impact on that KPI.

Results should be interpreted with the following in mind:

  • If a measure was implemented uniformly across all Living Labs, its individual effect may be harder to isolate, and results should be read with caution.
  • The mean squared error (MSE) of the model provides an indication of estimation accuracy. A lower MSE means the model fits the observed data more closely.

Results where updated on 9 Apr 2026, 12:28

1

Interest Domain for Analysis

The KPIs have been grouped by scope of interest

Please select analysis conditions to view the results.

The impact levels reported by this assessment tool are algorithmic estimates derived from implemented measures and observed KPI changes. They serve as indicative values and may not exactly reflect real-world outcomes.