BIN-CT: Urban Waste Collection based in Predicting the Container Fill Level

TL;DR

BIN-CT is an intelligent waste collection system that predicts container fill levels and optimizes routes, reducing costs, emissions, and improving service quality in urban waste management.

Contribution

The paper introduces BIN-CT, a novel software system that combines fill level prediction with route optimization for efficient urban waste collection.

Findings

Reduces waste collection costs and fuel consumption.

Decreases harmful emissions from waste collection trucks.

Improves service quality by preventing container overflows.

Abstract

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.