Enhancing door-to-door waste collection forecasting through ML.

We explore the application of machine learning (ML) techniques to forecast door-to-door waste collection, addressing the challenges in municipal solid waste (MSW) management. ML models offer a promising solution to optimize waste collection operations, especially amid growing urban populations and evolving waste generation rates. Leveraging comprehensive data from a northeastern Italian municipality, including various waste types, our study investigates ML algorithms' efficacy in predicting household waste collection requirements. We examine two key tasks: predicting daily waste exposure likelihood and forecasting fulfilled pickups over monthly and weekly periods. Both tasks are developed at the user level, forecasting user behavior based on features that describe the user. We split the data based on its temporal distribution and evaluated the models by forecasting user behavior in a future period, using the data from earlier periods to train the models. This study addresses a novel and challenging scenario, as, to the best of our knowledge, no prior work has specifically focused on door-to-door waste management using machine learning techniques. Results highlight ML models' potential in enhancing waste collection efficiency, aiding route planning, resource allocation, and environmental sustainability in urban areas. Additionally, our findings underscore the importance of tailoring strategies to waste categories and pickup frequencies for optimal performance.