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Conference Paper: A Data Mining and Optimization-based Real-time Mobile Intelligent Routing System for City Logistics

TitleA Data Mining and Optimization-based Real-time Mobile Intelligent Routing System for City Logistics
Authors
Issue Date2013
PublisherI E E E.
Citation
The IEEE 8th International Conference on Industrial and Information Systems (ICIIS), Peradeniya, USA, 17-20 December 2013. In IEEE International Conference on Industrial and Information Systems Proceedings, 2013, p. 156-161, article no. 6731973 How to Cite?
AbstractCity logistics is facing the challenging problem of providing a quick-response and on-time delivery service in congested urban areas with frequent traffic jams. The dynamically changing traffic conditions make the predetermined best transportation plans suboptimal and consequently cause increased logistics cost and even greater air pollution. To help the driver determine time-optimal routing solutions in order to avoid congestion according to the real-time traffic flow, a Real-time Mobile Intelligent Routing System is designed and deployed on drivers' Smartphones to help in routing decision making. Data mining techniques are employed to discover the routing patterns from the past cases of routing plans so as to generate case-based routing plans for the drivers. A metaheuristic is used to undertake the optimization of a real-time optimal routing plan based on real-time traffic information. A case study and computational experiments demonstrate the effectiveness of the proposed methods in significantly reducing the traveling time.
Persistent Identifierhttp://hdl.handle.net/10722/203996
ISBN

 

DC FieldValueLanguage
dc.contributor.authorLin, Cen_US
dc.contributor.authorChoy, KLen_US
dc.contributor.authorPang, GKHen_US
dc.contributor.authorNg, MTWen_US
dc.date.accessioned2014-09-19T20:01:29Z-
dc.date.available2014-09-19T20:01:29Z-
dc.date.issued2013en_US
dc.identifier.citationThe IEEE 8th International Conference on Industrial and Information Systems (ICIIS), Peradeniya, USA, 17-20 December 2013. In IEEE International Conference on Industrial and Information Systems Proceedings, 2013, p. 156-161, article no. 6731973en_US
dc.identifier.isbn9781479909100-
dc.identifier.urihttp://hdl.handle.net/10722/203996-
dc.description.abstractCity logistics is facing the challenging problem of providing a quick-response and on-time delivery service in congested urban areas with frequent traffic jams. The dynamically changing traffic conditions make the predetermined best transportation plans suboptimal and consequently cause increased logistics cost and even greater air pollution. To help the driver determine time-optimal routing solutions in order to avoid congestion according to the real-time traffic flow, a Real-time Mobile Intelligent Routing System is designed and deployed on drivers' Smartphones to help in routing decision making. Data mining techniques are employed to discover the routing patterns from the past cases of routing plans so as to generate case-based routing plans for the drivers. A metaheuristic is used to undertake the optimization of a real-time optimal routing plan based on real-time traffic information. A case study and computational experiments demonstrate the effectiveness of the proposed methods in significantly reducing the traveling time.-
dc.languageengen_US
dc.publisherI E E E.-
dc.relation.ispartofInternational Conference on Industrial and Information Systemsen_US
dc.rightsInternational Conference on Industrial and Information Systems. Copyright © I E E E.-
dc.rights©2013 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.titleA Data Mining and Optimization-based Real-time Mobile Intelligent Routing System for City Logisticsen_US
dc.typeConference_Paperen_US
dc.identifier.emailPang, GKH: gpang@eee.hku.hken_US
dc.identifier.authorityPang, GKH=rp00162en_US
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/ICIInfS.2013.6731973-
dc.identifier.scopuseid_2-s2.0-84894453562-
dc.identifier.hkuros236054en_US
dc.identifier.spage156, article no. 6731973en_US
dc.identifier.epage161, article no. 6731973en_US
dc.publisher.placeUnited States-

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