The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Ces dernières années, les techniques des systèmes de transport intelligents (STI) ont été largement exploitées pour améliorer la qualité des services publics. En tant que l'un des leaders mondiaux du recyclage, Taiwan adopte une politique de collecte et d'élimination des déchets appelée « les déchets ne touchent pas le sol », qui oblige le public à livrer les déchets directement aux points de collecte en attente de collecte des ordures. Cette étude développe un système de prédiction du temps de trajet basé sur le regroupement de données pour fournir des informations en temps réel sur l'heure d'arrivée du véhicule de collecte des déchets (WCV). Le système développé se compose d'appareils mobiles (MD), d'unités embarquées (OBU), d'un serveur de gestion de flotte (FMS) et d'un serveur d'analyse de données (DAS). Un modèle de prédiction du temps de trajet utilisant la technique de regroupement adaptatif couplée à une procédure de sélection de caractéristiques de données est conçu et intégré dans le DAS. Tout en recevant des demandes des MD des utilisateurs et des données pertinentes provenant des OBU des WCV via le FMS, le DAS exécute le modèle conçu pour produire l'heure d'arrivée prévue du WCV. Le résultat de notre expérience démontre que le modèle de prédiction proposé atteint un taux de précision de 75.0 % et surpasse la méthode de régression linéaire de référence et la technique des réseaux neuronaux, dont les taux de précision sont respectivement de 14.7 % et 27.6 %. Le système développé est à la fois efficace et efficient et a été mis en ligne.
Chi-Hua CHEN
Fuzhou University
Feng-Jang HWANG
University of Technology Sydney
Hsu-Yang KUNG
National Pingtung University of Science and Technology
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Chi-Hua CHEN, Feng-Jang HWANG, Hsu-Yang KUNG, "Travel Time Prediction System Based on Data Clustering for Waste Collection Vehicles" in IEICE TRANSACTIONS on Information,
vol. E102-D, no. 7, pp. 1374-1383, July 2019, doi: 10.1587/transinf.2018EDP7299.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2018EDP7299/_p
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@ARTICLE{e102-d_7_1374,
author={Chi-Hua CHEN, Feng-Jang HWANG, Hsu-Yang KUNG, },
journal={IEICE TRANSACTIONS on Information},
title={Travel Time Prediction System Based on Data Clustering for Waste Collection Vehicles},
year={2019},
volume={E102-D},
number={7},
pages={1374-1383},
abstract={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.},
keywords={},
doi={10.1587/transinf.2018EDP7299},
ISSN={1745-1361},
month={July},}
Copier
TY - JOUR
TI - Travel Time Prediction System Based on Data Clustering for Waste Collection Vehicles
T2 - IEICE TRANSACTIONS on Information
SP - 1374
EP - 1383
AU - Chi-Hua CHEN
AU - Feng-Jang HWANG
AU - Hsu-Yang KUNG
PY - 2019
DO - 10.1587/transinf.2018EDP7299
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E102-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2019
AB - 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.
ER -