2024-03-29T06:45:43Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/69302022-11-08T09:08:01Zcom_10259.4_104com_10259_2604col_10259_6848
Prediction of container filling for the selective waste collection in Algeciras (Spain)
Rodríguez López, Juana Carmen
Moscoso López, José Antonio
Ruiz Aguilar, Juan Jesús
Rodríguez García, Inmaculada
Alcántara Pérez, Jose Manuel
Turias Domínguez, Ignacio J.
Modelización
Simulación
Transporte marítimo
Modelling
Simulation
Maritime transport
The aim of this study is to create an intelligent system that improves the efficiency of garbage
collection, (cardboard waste, in this particular case). The number of cardboard containers to
be collected each day will be determined based on a prediction made on the filled volume
recorded in each container. It will be reflected in the cost and fuel savings, reducing
emissions and contributing to environmental sustainability. These results will allow planning
the sequence of waste removal, which means the optimal collection route considering
restrictive parameters such as the type of truck, the location of containers, collection times
by zones, and the availability of working staff.
A filling prediction system is proposed based on real historical data provided by the current
waste collection company in Algeciras (ARCGISA). To achieve this objective, an intelligent
system is designed using predictive analytics and several methods based on machine
learning, modelling the collection system as a classification model, comparing the results
from a statistical point of view (using sensitivity, specificity, etc.). The results obtained with
the best-tested method indicate an improvement average rate of 26% in sensitivity
performance index and 67% in specificity performance index.
Currently, waste collection is carried out without predictive analysis. The relevance of an
efficient waste collection system is becoming increasingly important. Achieving optimal
waste collection will result in improved service to citizens, cost savings for the
administration, and significant environmental improvements.
2022-09-20T06:47:07Z
2022-09-20T06:47:07Z
2022-09-20T06:47:07Z
2021-07
info:eu-repo/semantics/conferenceObject
978-84-18465-12-3
http://hdl.handle.net/10259/6930
10.36443/10259/6930
eng
R-Evolucionando el transporte
http://hdl.handle.net/10259/6490
https://doi.org/10.36443/9788418465123
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-098160-B-I00/ES/DEEP LEARNING IN AIR POLLUTION FORECASTING
info:eu-repo/semantics/openAccess
Universidad de Burgos. Servicio de Publicaciones e Imagen Institucional