The Design and Implementation of a Garbage Truck Fleet Management System

Abstract:With the rise and development of information technology (IT) services, the amount of data generated is rapidly increasing. Data from many different places are inconsistent. Data capture, storage and analysis have major challenges. Most data analysis methods are unable to handle such large amounts of data. Many studies employ neural networks, mostly specifying the number of hidden layers and neurons according to experience or formula. Different sets of network topologies have different results, and the best network model is selected. This investigation proposes a system based on the ensemble neural network (ENN). It creates multiple network models, each with different numbers of hidden layers and neurons. A model that does not achieve the accuracy rate is discarded. The proposed system derives the weighted average of all remaining network models to improve the accuracy of the prediction. This study applies the proposed method to generate agricultural yield predictions. The agricultural production process in Taiwan is more complex than those of manufacturing or other industries. The Council of Agriculture provides agricultural forecasting primarily based on the planted area and experience to predict the yield, but without consideration of the overall planting environment. This work applies the proposed data analysis method to agriculture. The method based on ENN has a much lower error rate than traditional back-propagation neural networks, while multiple regression analysis has an error rate of 12.4%. Experimental results reveal that the ENN method is better than traditional back-propagation neural networks and multiple regression analysis.

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“…This is based on mathematical principles, and can be categorized as descriptive statistics and inferential statistics [5][6][7].…”

Section: Discussionmentioning

confidence: 99%

Accuracy Analysis Mechanism for Agriculture Data Using the Ensemble Neural Network Method

et al. 2016

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“…Dibuat menggunakan Bahasa program android dan MySQL sebagai database. Penelitian oleh C.H.Chen dkk [10], mengusulkan sistem manajemen armada truk sampah yang mencakup On-Board Unit (OBU) pada truk sampah, Sistem Manajemen Armada (FMS) dan GTA dengan optional mekanisme hemat daya untuk memperbaharui pesan berdasarkan informasi lokasi. OBU dilengkapi dengan modul sistem penentuan posisi global (GPS) dengan modul jaringan selular untuk menyediakan data deteksi kedatangan segera dan mengirim lokasi truk sampah ke FMS melalui GPRS.…”

Section: Pendahuluanunclassified

Sistem Informasi Geografis Lokasi Jalur Layanan Armada Kebersihan Berbasis Android Dinas Lingkungan Hidup Kota Gorontalo

Utiarahman

Alang

Malago

2021

JSAI

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Perkara lingkungan bersih, tak lepas daripada sampah yang kian menumpuk. Bertambahnya penduduk, meluasnya pembangunan, merupakan diantara faktor yang menyebabkan penumpukan sampah. Salah satu misi pemerintah mungkin ingin warganya memiliki kenyamana untuk bisa mengakses informasi tentang pembuangan sampah, seperti lokasi tempat pengumpulan sampah sementara, lokasi armada kebersihan, serta rute tertentu. Oleh karena itu studi mengusulkan untuk merancang suatu sistem informasi geografis lokasi jalur armada kebersihan berbasis android yang mudah diakses oleh masyarakat melalui smartphone. Bahasa program Android dan PHP digunakan kemudian dikombinasikan MySQL sebagai wadah penampung data. Aplikasi yang dirancang diharapkan dapat membantu masyarakat dan Dinas Lingkungan Hidup kota Gorontalo secara sinergi berjalan beriringan mewujudkan tata kelola sampah yang baik (good garbage management) efisien dan efektif.

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“…In recent years, intelligent transportation system (ITS) techniques have been widely exploited to enhance the quality of public services, e.g. mass rapid transit [1], railway traffic [2], bus system [3], bicycle-sharing system [4], and garbage truck fleet management [5], etc. One of these applications which have not been extensively studied could be the utilization of transportation information in waste collection service.…”

Section: Introductionmentioning

confidence: 99%

Travel Time Prediction System Based on Data Clustering for Waste Collection Vehicles

Chen

Hwang

Kung

2019

IEICE Trans. Inf. & Syst.

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In recent years, intelligent transportation system (ITS) techniques have been widely exploited to enhance the quality of public services. As one of the worldwide leaders in recycling, Taiwan adopts the waste collection and disposal policy named "trash doesn't touch the ground", which requires the public to deliver garbage directly to the collection points for awaiting garbage collection. This study develops a travel time prediction system based on data clustering for providing real-time information on the arrival time of waste collection vehicle (WCV). The developed system consists of mobile devices (MDs), on-board units (OBUs), a fleet management server (FMS), and a data analysis server (DAS). A travel time prediction model utilizing the adaptive-based clustering technique coupled with a data feature selection procedure is devised and embedded in the DAS. While receiving inquiries from users' MDs and relevant data from WCVs' OBUs through the FMS, the DAS performs the devised model to yield the predicted arrival time of WCV. Our experiment result demonstrates that the proposed prediction model achieves an accuracy rate of 75.0% and outperforms the reference linear regression method and neural network technique, the accuracy rates of which are 14.7% and 27.6%, respectively. The developed system is effective as well as efficient and has gone online.

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The Design and Implementation of a Garbage Truck Fleet Management System

Cited by 6 publications

References 16 publications

Accuracy Analysis Mechanism for Agriculture Data Using the Ensemble Neural Network Method

Accuracy Analysis Mechanism for Agriculture Data Using the Ensemble Neural Network Method

Sistem Informasi Geografis Lokasi Jalur Layanan Armada Kebersihan Berbasis Android Dinas Lingkungan Hidup Kota Gorontalo

Travel Time Prediction System Based on Data Clustering for Waste Collection Vehicles

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