BIN-CT: Urban Waste Collection based in Predicting the Container Fill Level
This addresses the high operational costs and environmental impact of waste management for cities, but it is incremental as it applies existing computational learning methods to a specific domain.
The authors tackled the problem of optimizing urban waste collection by predicting container fill levels, resulting in reduced travel distances, fuel consumption, and costs, with a real case study in a Spanish city showing avoidance of unnecessary visits and lower emissions.
The fast demographic growth, together with the concentration of the population in cities and the increasing amount of daily waste, are factors that push to the limit the ability of waste assimilation by Nature. Therefore, we need technological means to make an optimal management of the waste collection process, which represents 70% of the operational cost in waste treatment. In this article, we present a free intelligent software system, based on computational learning algorithms, which plans the best routes for waste collection supported by past (historical) and future (predictions) data. The objective of the system is the cost reduction of the waste collection service by means of the minimization in distance traveled by any truck to collect a container, hence the fuel consumption. At the same time the quality of service to the citizen is increased avoiding the annoying overflows of containers thanks to the accurate fill level predictions performed by BIN-CT. In this article we show the features of our software system, illustrating it operation with a real case study of a Spanish city. We conclude that the use of BIN-CT avoids unnecessary visits to containers, reduces the distance traveled to collect a container and therefore we obtain a reduction of total costs and harmful emissions thrown to the atmosphere.