BIN-CT: Urban Waste Collection based in Predicting the Container Fill Level
The fast demographic growth, together with the concentration of the population in cities and the increasing amount of daily waste, are factors that push to the limit the ability of waste assimilation by Nature. Therefore, we need technological means to make an optimal management 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, 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 the cost reduction of the waste collection service by means of the minimization in distance traveled by any truck to collect a container, hence the fuel consumption. At the same time the quality of service to the citizen is increased avoiding the annoying overflows of containers thanks to the accurate fill level predictions performed by BIN-CT. In this article we show the features of our software system, illustrating it operation with a real case study of a Spanish city. We conclude that the use of BIN-CT avoids unnecessary visits to containers, reduces the distance traveled to collect a container and therefore we obtain a reduction of total costs and harmful emissions thrown to the atmosphere.
Code (0)
등록된 구현이 없습니다.
Tasks
ManagementSimilar Papers 제목 키워드 기반
StreetView-Waste: A Multi-Task Dataset for Urban Waste Management
Urban waste management remains a critical challenge for the development of smart cities. Despite the growing number of litter detection datasets, the problem of monitoring overflowing waste containers, particularly from …
Object DetectionSpatial Impulse Response Analysis and Ensemble Learning for Efficient Precision Level Sensing
In this paper, we propose an innovative method for determining the fill level of containers, such as trash cans, addressing a critical aspect of waste management. The method combines spatial impulse response analysis wit…
Ensemble LearningManagementThe Future of Sustainability in Germany: Areas for Improvement and Innovation
This paper reviews the literature on biodegradable waste management in Germany, a multifaceted endeavor that reflects its commitment to sustainability and environmental responsibility. It examines the processes and benef…
ManagementResidual Reinforcement Learning for Waste-Container Lifting Using Large-Scale Cranes with Underactuated Tools
This paper studies the container lifting phase of a waste-container recycling task in urban environments, performed by a hydraulic loader crane equipped with an underactuated discharge unit, and proposes a residual reinf…
Reinforcement LearningSmart Waste Management System for Makkah City using Artificial Intelligence and Internet of Things
Waste management is a critical global issue with significant environmental and public health implications. It has become more destructive during large-scale events such as the annual pilgrimage to Makkah, Saudi Arabia, o…
Management