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Related Experiment Video

Updated: Jan 26, 2026

Design and Construction of an Urban Runoff Research Facility

Design and Construction of an Urban Runoff Research Facility

Published on: August 8, 2014

BIN-CT: Urban waste collection based on predicting the container fill level.

Javier Ferrer¹, Enrique Alba¹

¹Dept. Lenguajes y Ciencias de la Computación, University of Malaga, Spain.

|April 21, 2019

Summary

This study introduces BIN-CT, intelligent waste collection software that optimizes routes using predictive data. The system significantly cuts costs and emissions by reducing travel distance and preventing container overflows.

Keywords:

Forecasting Machine learning Recycling Routes generation Waste collection

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Last Updated: Jan 26, 2026

Design and Construction of an Urban Runoff Research Facility

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Published on: August 8, 2014

A Protocol for Computer-Based Protein Structure and Function Prediction

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Published on: November 3, 2011

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

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Published on: January 20, 2023

Area of Science:

Environmental Science
Computer Science
Operations Research

Background:

Rapid population growth and urbanization strain natural waste assimilation capacities.
Waste collection logistics represent a significant operational cost (70%) in waste management.
Efficient waste management is crucial for urban sustainability and public health.

Purpose of the Study:

To introduce BIN-CT (BIN for the CiTy), an intelligent software system for optimizing waste collection routes.
To reduce operational costs and fuel consumption in waste collection services.
To improve service quality by preventing container overflows through accurate fill-level predictions.

Main Methods:

Development of a free intelligent software system, BIN-CT, utilizing computational learning algorithms.
Integration of historical and predictive data for route planning.
Application and case study validation in a Spanish city.

Main Results:

BIN-CT reduces the distance traveled for waste collection by 20%.
Generated routes are 33.2% shorter compared to existing company routes.
The system effectively avoids unnecessary trips to waste containers.

Conclusions:

BIN-CT offers a technological solution for optimizing urban waste collection.
The software enables substantial cost reductions and minimizes harmful atmospheric emissions.
Improved route planning and fill-level predictions enhance service quality and efficiency.