RT conference output
T1 Prediction of container filling for the selective waste collection in Algeciras (Spain)
A1 Rodríguez López, Juana Carmen
A1 Rodríguez García, María Inmaculada
A1 Moscoso López, José Antonio
A1 Ruiz Águilar, Juan Jesús
A1 Alcántara Pérez, José Manuel
A1 Turias Domínguez, Ignacio José
A2 Ingeniería Industrial e Ingeniería Civil
A2 Ingeniería Informática
K1 artificial neural networks
K1 prediction
K1 waste collection
AB 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. © 2021 Elsevier B.V.. All rights reserved
PB ELSEVIER
SN 2352-1457
YR 2021
FD 2021
LK http://hdl.handle.net/10498/27002
UL http://hdl.handle.net/10498/27002
LA eng
NO 14th Conference on Transport Engineering: 6th – 8th July 2021
DS Repositorio Institucional de la Universidad de Cádiz
RD 21-sep-2026