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

By Javier Ferrer, Enrique Alba. 2018

44 citations recorded by the source corpus.

The fast demographic growth, together with the population concentration in cities and the increasing amount of daily waste, are factors that are pushing to the limit the ability of waste assimilation by Nature. Therefore, we need technological means to optimally manage 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 called BIN-CT (BIN for the CiTy), 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 to reduction the cost of the waste collection service minimizing the distance traveled by a truck to collect the waste from a container, thereby reducing the fuel consumption. At the same time the quality of service for the citizen is increased, avoiding the annoying overflows of containers thanks to the accurate fill-level predictions given by BIN-CT. In this article we show the features of our software system, illustrating its operation with a real case study of a Spanish city. We conclude that the use of BIN-CT avoids unnecessary trips to containers, reduces the distance traveled to collect a container by 20%, and generates routes 33.2% shorter than the routes used by the company. Therefore it enables a considerable reduction of total costs and harmful emissions thrown up into the atmosphere.

Subfields: Building and Construction, Industrial and Manufacturing Engineering, Management Science and Operations Research

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Building and Construction

1. Andrés Camero
2. Enrique Alba
3. Jamal Toutouh
4. Javier Ferrer

In Industrial and Manufacturing Engineering

1. Andrés Camero
2. Jamal Toutouh
3. Enrique Alba
4. Javier Ferrer

In Management Science and Operations Research

1. Andrés Camero
2. Jamal Toutouh
3. Enrique Alba
4. Javier Ferrer
5. Thomas Bäck

Citations in the corpus

• Bayesian Neural Architecture Search using A Training-Free Performance Metric (2020, background): “It is important to notice that this problem has been used as a benchmark in several studies [17, 9, 8] and that it is a real-world problem.”
• Random error sampling-based recurrent neural network architecture optimization (2019, background): “prediction problem [41], and the coal-fired power plant flame intensity prediction problem [38].”
• Waste generation prediction under uncertainty in smart cities through deep neuroevolution (2019, methodology): “To validate our proposal, we analyze a real-world case consisting of more than two hundred waste containers located in a city in Spain, and we compare our results against the ones presented by [17].”
• A Specialized Evolutionary Strategy Using Mean Absolute Error Random Sampling to Design Recurrent Neural Networks (2019, methodology): “We studied the problem presented by Ferrer and Alba [44], [45], where the filling level of 217 paper containers spread out in a city in Spain is predicted.”