EcoRouteAI: Optimizing Ecopoints with Artificial Intelligence

Portugal's Strategic Plan for Urban Waste (PERSU) 2030 established ambitious targets for waste management, aimed at increasing recycling rates and improving environmental sustainability. However, current recycling and waste separation rates remain below targets at both national and European levels, creating significant challenges to meeting these objectives.

In response, an innovative project is being developed in collaboration with ALGAR, focused on increasing the efficiency of selective waste collection in the Algarve. This initiative leverages Artificial Intelligence to optimize ecopoint location, making selective collection more efficient and accessible to the population.

The system uses a machine learning approach to analyze multimodal data, including demographic data, geospatial information, and satellite and street-level imagery. These data sources provide a comprehensive and detailed view of urban areas, enabling identification of optimal locations for new ecopoint installations.

Additionally, predictive models were created to estimate waste generation in new areas, and street-level image data is used to assess location viability, ensuring installations meet necessary requirements for container placement. The system also incorporates heuristic optimization models that analyze the existing ecopoint network and suggest adjustments to maximize waste collection efficiency.

The impact of this solution is significant. In a pilot phase across Lagoa and São Brás de Alportel municipalities, the AI system demonstrated over 80% alignment between its recommendations and decisions made by human experts. Based on these results, the goal is to expand implementation across the entire Algarve region by the end of 2024, with plans to eventually scale the project to other areas of the country.

This project improves local waste management while contributing toward national and European sustainability targets. By increasing population access to selective collection, the solution drives higher participation in recycling, resulting in significant increases in recycled waste volumes. Additionally, the system optimizes available resources, reducing time and costs involved in identifying new ecopoint locations and improving collection efficiency.

Implementing this type of technology also creates new opportunities. In future phases, the system could suggest relocation of existing ecopoints and optimize collection routes, dynamically adjusting processes to address daily needs of both the population and waste collection companies.

As the project scales, there is significant potential for expansion to other areas and municipalities, targeting coverage of up to 60% of Portugal's population. This impact underscores the importance of investing in advanced technological solutions, such as AI, to address complex challenges related to waste management and sustainability.