2019 |
Ferrer, Javier; Alba, Enrique BIN-CT: Urban waste collection based on predicting the container fill level. Journal Article BioSystems, 186 , pp. 103962, 2019, ISSN: 1872-8324. Abstract | Links | BibTeX | Tags: Forecasting, Machine learning, Recycling, Routes generation, Waste collection @article{Ferrer2018Bio, title = {BIN-CT: Urban waste collection based on predicting the container fill level.}, author = {Javier Ferrer and Enrique Alba}, url = {http://arxiv.org/abs/1807.01603 https://linkinghub.elsevier.com/retrieve/pii/S0303264718301333 http://dx.doi.org/10.1016/j.biosystems.2019.04.006 http://www.ncbi.nlm.nih.gov/pubmed/31004697}, doi = {10.1016/j.biosystems.2019.04.006}, issn = {1872-8324}, year = {2019}, date = {2019-12-01}, journal = {BioSystems}, volume = {186}, pages = {103962}, abstract = {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.}, keywords = {Forecasting, Machine learning, Recycling, Routes generation, Waste collection}, pubstate = {published}, tppubtype = {article} } 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. |
Camero, Andrés; Toutouh, Jamal; Ferrer, Javier; Alba, Enrique Waste generation prediction under uncertainty in smart cities through deep neuroevolution Journal Article Revista Facultad de Ingenieria, (93), pp. 128–138, 2019, ISSN: 24222844. Abstract | Links | BibTeX | Tags: Deep learning, Deep neuroevolution, evolutionary algorithms, Smart cities, Waste collection @article{Camero2019b, title = {Waste generation prediction under uncertainty in smart cities through deep neuroevolution}, author = {Andrés Camero and Jamal Toutouh and Javier Ferrer and Enrique Alba}, doi = {10.17533/udea.redin.20190736}, issn = {24222844}, year = {2019}, date = {2019-01-01}, journal = {Revista Facultad de Ingenieria}, number = {93}, pages = {128--138}, abstract = {The unsustainable development of countries has created a problem due to the unstoppable waste generation. Moreover, waste collection is carried out following a pre-defined route that does not take into account the actual level of the containers collected. Therefore, optimizing the way the waste is collected presents an interesting opportunity. In this study, we tackle the problem of predicting the waste generation ratio in real-world conditions, i.e., under uncertainty. Particularly, we use a deep neuroevolutionary technique to automatically design a recurrent network that captures the filling level of all waste containers in a city at once, and we study the suitability of our proposal when faced to noisy and faulty data. We validate our proposal using a real-world case study, consisting of more than two hundred waste containers located in a city in Spain, and we compare our results to the state-of-the-art. The results show that our approach exceeds all its competitors and that its accuracy in a real-world scenario, i.e., under uncertain data, is good enough for optimizing the waste collection planning.}, keywords = {Deep learning, Deep neuroevolution, evolutionary algorithms, Smart cities, Waste collection}, pubstate = {published}, tppubtype = {article} } The unsustainable development of countries has created a problem due to the unstoppable waste generation. Moreover, waste collection is carried out following a pre-defined route that does not take into account the actual level of the containers collected. Therefore, optimizing the way the waste is collected presents an interesting opportunity. In this study, we tackle the problem of predicting the waste generation ratio in real-world conditions, i.e., under uncertainty. Particularly, we use a deep neuroevolutionary technique to automatically design a recurrent network that captures the filling level of all waste containers in a city at once, and we study the suitability of our proposal when faced to noisy and faulty data. We validate our proposal using a real-world case study, consisting of more than two hundred waste containers located in a city in Spain, and we compare our results to the state-of-the-art. The results show that our approach exceeds all its competitors and that its accuracy in a real-world scenario, i.e., under uncertain data, is good enough for optimizing the waste collection planning. |
2019 |
BIN-CT: Urban waste collection based on predicting the container fill level. Journal Article BioSystems, 186 , pp. 103962, 2019, ISSN: 1872-8324. |
Waste generation prediction under uncertainty in smart cities through deep neuroevolution Journal Article Revista Facultad de Ingenieria, (93), pp. 128–138, 2019, ISSN: 24222844. |