2026-09-15T21:00:30Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/69302024-05-20T07:54:57Zcom_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