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Article: GPS trajectory data segmentation based on probabilistic logic

TitleGPS trajectory data segmentation based on probabilistic logic
Authors
KeywordsGPS trajectory data segmentation
Newton's method
Probabilistic logic
Issue Date2018
PublisherElsevier Inc. The Journal's web site is located at http://www.elsevier.com/locate/ijar
Citation
International Journal of Approximate Reasoning, 2018, v. 103, p. 227-247 How to Cite?
AbstractWith the rapid development of internet economy, transparent logistics is stepping into a prosperity period with massive transportation data generated and collected every day. In this paper, we focus on the segmentation of GPS trajectory data generated in logistics transportation to analyze the vehicle behaviors and extract business affair information according to the vehicle behavior characteristics, which is challenging due to the complexity of trajectory data and unavailability of road information. We extract the stopping points from the trajectory data sequence based on the duration of nonmovement, and construct business time window and electronic fence by analyzing the driving habits of vehicles. Furthermore, we propose a probabilistic logic based data segmentation method (PLDSM) which not only helps finding all the business points but also assists in inferring the business affair categories. An efficient numerical algorithm integrating duality theory and Newton's method is proposed to obtain the optimal solution. Finally, a practical example is presented to validate the effectiveness of PLDSM. The results greatly enrich the data segmentation technique and promote the practicability of probabilistic logic. © 2018 Elsevier Inc.
Persistent Identifierhttp://hdl.handle.net/10722/264182
ISSN
2019 Impact Factor: 2.678
2015 SCImago Journal Rankings: 2.304
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorGuo, S-
dc.contributor.authorLi, X-
dc.contributor.authorChing, WK-
dc.contributor.authorDan, R-
dc.contributor.authorLi, WK-
dc.contributor.authorZhang, Z-
dc.date.accessioned2018-10-22T07:50:51Z-
dc.date.available2018-10-22T07:50:51Z-
dc.date.issued2018-
dc.identifier.citationInternational Journal of Approximate Reasoning, 2018, v. 103, p. 227-247-
dc.identifier.issn0888-613X-
dc.identifier.urihttp://hdl.handle.net/10722/264182-
dc.description.abstractWith the rapid development of internet economy, transparent logistics is stepping into a prosperity period with massive transportation data generated and collected every day. In this paper, we focus on the segmentation of GPS trajectory data generated in logistics transportation to analyze the vehicle behaviors and extract business affair information according to the vehicle behavior characteristics, which is challenging due to the complexity of trajectory data and unavailability of road information. We extract the stopping points from the trajectory data sequence based on the duration of nonmovement, and construct business time window and electronic fence by analyzing the driving habits of vehicles. Furthermore, we propose a probabilistic logic based data segmentation method (PLDSM) which not only helps finding all the business points but also assists in inferring the business affair categories. An efficient numerical algorithm integrating duality theory and Newton's method is proposed to obtain the optimal solution. Finally, a practical example is presented to validate the effectiveness of PLDSM. The results greatly enrich the data segmentation technique and promote the practicability of probabilistic logic. © 2018 Elsevier Inc.-
dc.languageeng-
dc.publisherElsevier Inc. The Journal's web site is located at http://www.elsevier.com/locate/ijar-
dc.relation.ispartofInternational Journal of Approximate Reasoning-
dc.subjectGPS trajectory data segmentation-
dc.subjectNewton's method-
dc.subjectProbabilistic logic-
dc.titleGPS trajectory data segmentation based on probabilistic logic-
dc.typeArticle-
dc.identifier.emailChing, WK: wching@hku.hk-
dc.identifier.emailLi, WK: hrntlwk@hkucc.hku.hk-
dc.identifier.emailZhang, Z: zhangzw@hku.hk-
dc.identifier.authorityChing, WK=rp00679-
dc.identifier.authorityLi, WK=rp00741-
dc.identifier.authorityZhang, Z=rp02087-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.ijar.2018.09.008-
dc.identifier.scopuseid_2-s2.0-85054590269-
dc.identifier.hkuros295573-
dc.identifier.volume103-
dc.identifier.spage227-
dc.identifier.epage247-
dc.identifier.isiWOS:000452582000014-
dc.publisher.placeUnited States-

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