Travel time prediction system based on data clustering for waste collection vehicles

Chen, CH; Hwang, FJ; Kung, HY

Travel time prediction system based on data clustering for waste collection vehicles

Chen, CH Hwang, FJ

Kung, HY

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Publication Type:: Journal Article
Citation:: IEICE Transactions on Information and Systems, 2019, E102D (7), pp. 1374 - 1383
Issue Date:: 2019-01-01

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Field	Value	Language
dc.contributor.author	Chen, CH	en_US
dc.contributor.author	Hwang, FJ https://orcid.org/0000-0002-1741-5590	en_US
dc.contributor.author	Kung, HY	en_US
dc.date.available	2021-04-01T18:00:38Z
dc.date.issued	2019-01-01	en_US
dc.identifier.citation	IEICE Transactions on Information and Systems, 2019, E102D (7), pp. 1374 - 1383	en_US
dc.identifier.issn	0916-8532	en_US
dc.identifier.uri	http://hdl.handle.net/10453/132067
dc.description.abstract	Copyright © 2019 The Institute of Electronics, Information and Communication Engineers 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.	en_US
dc.relation.ispartof	IEICE Transactions on Information and Systems	en_US
dc.relation.isbasedon	10.1587/transinf.2018EDP7299	en_US
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject.classification	Information Systems	en_US
dc.title	Travel time prediction system based on data clustering for waste collection vehicles	en_US
dc.type	Journal Article
utslib.citation.volume	7	en_US
utslib.citation.volume	E102D	en_US
utslib.for	0806 Information Systems	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	1801 Law	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
utslib.copyright.status	open_access	*
pubs.issue	7	en_US
pubs.publication-status	Published	en_US
pubs.volume	E102D	en_US

Abstract:

Copyright © 2019 The Institute of Electronics, Information and Communication Engineers 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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http://hdl.handle.net/10453/132067