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不同垃圾桶监测方法的比较:一项探索性研究。

Comparison of different waste bin monitoring approaches: An exploratory study.

机构信息

Department of Electrical and Computer Engineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal.

Centre for Management Studies (CEGIST), Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal.

出版信息

Waste Manag Res. 2023 Oct;41(10):1570-1583. doi: 10.1177/0734242X231160691. Epub 2023 May 3.

Abstract

Waste bin monitoring solutions are an essential step towards smart cities. This study presents an exploratory analysis of two waste bin monitoring approaches: (1) ultrasonic sensors installed in the bins and (2) visual observations (VO) of the waste collection truck drivers. Bin fill level data was collected from a Portuguese waste management company. A comparative statistical analysis of the two datasets (VO and sensor observations) was performed and a predictive model based on Gaussian processes was applied to enable a trade-off analysis of the number of collections versus the number of overflows for each monitoring approach. The results demonstrate that the VO are valuable and reveal that significant improvements can be achieved for either of the monitoring approaches in relation to the current situation. A monitoring approach based on VO combined with a predictive model is shown to be viable and leads to a considerable reduction in the number of collections and overflows. This approach can enable waste collection companies to improve their collection operations with minimal investment costs during their transition to fully sensorized bins.

摘要

垃圾桶监测解决方案是迈向智慧城市的重要一步。本研究对两种垃圾桶监测方法进行了探索性分析:(1)安装在垃圾桶中的超声波传感器,(2)垃圾清运车司机的视觉观察(VO)。垃圾桶装满水平数据是从一家葡萄牙废物管理公司收集的。对两个数据集(VO 和传感器观测值)进行了比较统计分析,并应用基于高斯过程的预测模型,以便对每种监测方法的收集次数与溢出次数进行权衡分析。结果表明,VO 是有价值的,并表明对于任何一种监测方法,与当前情况相比,都可以显著提高其性能。结果表明,基于 VO 的监测方法结合预测模型是可行的,可显著减少收集次数和溢出次数。这种方法可以使垃圾收集公司在向完全传感器化的垃圾桶过渡过程中,以最小的投资成本改进其收集操作。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f4d4/10517583/7ad394f94c28/10.1177_0734242X231160691-fig1.jpg

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