The project aims to reduce CO₂ emissions related to mobility. It focuses on supporting local governments in making informed decisions about sustainable transport policies, using a machine learning model based on Random Forest to predict how different interventions affect transport mode choice and emissions.
Context
Transport is one of the largest sources of greenhouse gas emissions. Although national decarbonization roadmaps exist, many key decisions are made by local governments, which often lack specific tools to assess the impact of their policies. In Gipuzkoa, where a large share of trips are made by car, reducing these emissions is essential to achieve climate goals (Figure 1).
Model
A model was developed using data from mobility surveys in Gipuzkoa, combined with geospatial and transport network data. A data enrichment process was applied to add value to the survey data and make the model more robust (Figure 2). The main transport modes considered were private vehicle, public transport, and walking.
A Random Forest model was chosen due to its ability to handle complex, non-linear relationships. A group cross-validation approach was used to verify model robustness. The model performed very well, achieving an F1 score of 82% (Figure 3). In addition, a SHAP analysis (Figure 4) was conducted to identify the most important variables in the model and inform policy design.
Interventions – 4 groups
1. Teleworking: Reduction in commuting through the option to work from home 2 or 4 days per week.
2. Public transport improvements: Reduction in travel time between 10% and 35%.
3. Job relocation: Bringing jobs closer to residential areas to encourage public transport and walking.
4. Vehicle electrification: Simulations with 25% and 50% electric vehicle penetration.
Results
Combined interventions were the most effective. For example, a scenario combining teleworking, public transport improvements, and vehicle electrification could reduce emissions by up to 58%. Individual measures, such as teleworking and electrification, reduce emissions by around 12% to 25%.
Interactive Tool
An interactive web interface was also developed to help local governments visualize the impact of these policies. The tool allows policymakers to explore different combinations of interventions and their effects on emissions and transport modes using environmental indicators and geospatial layers (Figure 5).
Conclusions
The project shows that combinations of interventions—especially land-use policies such as relocating workplaces closer to where people live—can have a particularly strong impact on CO₂ emissions. The use of an interactive tool supports data-driven decision-making, helping local governments design more effective mobility policies.