Path prediction of moving objects on road networks through analyzing past trajectories
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Sang-Wook | - |
dc.contributor.author | Won, Jung Im | - |
dc.contributor.author | Kim, Jong-Dae | - |
dc.contributor.author | Shin, Miyoung | - |
dc.contributor.author | Lee, Junghoon | - |
dc.contributor.author | Kim, Hanil | - |
dc.date.accessioned | 2022-12-21T06:24:49Z | - |
dc.date.available | 2022-12-21T06:24:49Z | - |
dc.date.created | 2022-09-16 | - |
dc.date.issued | 2007-09 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/179579 | - |
dc.description.abstract | This paper addresses a series of techniques for predicting a future path of an object moving on a road network. Most prior methods for future prediction mainly focus on the objects moving over Euclidean space. A variety of applications such as telematics, however, require us to handle the objects that move over road networks. In this paper, we propose a novel method for predicting a future path of an object in an efficient way by analyzing past trajectories whose changing pattern is similar to that of a current trajectory of a query object. For this purpose, we devise a new function for measuring a similarity between trajectories by considering the characteristics of road networks. By using this function, we search for candidate trajectories whose subtrajectories are similar to a given query trajectory by accessing past trajectories stored in moving object databases. Then, we predict a future path of a query object by analyzing the moving paths along with a current position to a destination of candidate trajectories. Also, we suggest a method that improves the accuracy of path prediction by grouping those moving paths whose differences are not significant. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Springer Verlag | - |
dc.title | Path prediction of moving objects on road networks through analyzing past trajectories | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Sang-Wook | - |
dc.contributor.affiliatedAuthor | Won, Jung Im | - |
dc.identifier.doi | 10.1007/978-3-540-74819-9_47 | - |
dc.identifier.scopusid | 2-s2.0-38049127167 | - |
dc.identifier.bibliographicCitation | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v.4692 LNAI, no.PART 1, pp.379 - 389 | - |
dc.relation.isPartOf | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | - |
dc.citation.title | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | - |
dc.citation.volume | 4692 LNAI | - |
dc.citation.number | PART 1 | - |
dc.citation.startPage | 379 | - |
dc.citation.endPage | 389 | - |
dc.type.rims | ART | - |
dc.type.docType | Conference Paper | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordPlus | Database systems | - |
dc.subject.keywordPlus | Object oriented programming | - |
dc.subject.keywordPlus | Pattern recognition | - |
dc.subject.keywordPlus | Query processing | - |
dc.subject.keywordPlus | Search engines | - |
dc.subject.keywordPlus | Tracking (position) | - |
dc.subject.keywordPlus | Candidate trajectories | - |
dc.subject.keywordPlus | Euclidean space | - |
dc.subject.keywordPlus | Query objects | - |
dc.subject.keywordPlus | Motion planning | - |
dc.identifier.url | https://link.springer.com/chapter/10.1007/978-3-540-74819-9_47 | - |
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