Predictive Urban Cleaning Optimization
Madrid, as the main city in Spain, faces the constant challenge of efficiently managing its urban waste, a crucial task for citizens' well-being and environmental sustainability. The municipal cleaning and waste collection service, operated by private entities, such as FCC, is essential in this daily battle. To overcome this challenge and improve its competitiveness, FCC seeks to employ advanced data analysis to anticipate specific cleaning needs in the metropolis, starting with a pilot project in the district of Usera.
From WhiteBox, we propose a novel "propensity to get dirty" indicator for Madrid, analyzing factors such as weather conditions, high-traffic events, mobility patterns, and the location of critical points of interest, including educational and health centers. This approach is based on the capture of open data combined with specific FCC data, taking advantage of cutting-edge technologies and open source analysis tools such as Statsmodels and Prophet to forecast the evolution of this indicator. This knowledge is translated into concrete actions through an interactive dashboard in Power BI, facilitating more effective management of the cleaning service and garbage collection. The entire system is supported by the robustness of Amazon Web Services, ensuring scalability and reliability through the use of Docker containers.
This project has marked a significant turning point for FCC and the Madrid City Council, allowing a real-time view of the state of street cleaning and allowing for more strategic and earlier planning of cleaning services. This advance has not only improved the efficiency and profitability of operations, but it has significantly raised the quality of life of Madrid residents, reaffirming FCC's commitment to innovation and urban sustainability.
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