Path prediction of moving objects on road networks through analyzing past trajectories
- Authors
- Kim, Sang-Wook; Won, Jung Im; Kim, Jong-Dae; Shin, Miyoung; Lee, Junghoon; Kim, Hanil
- Issue Date
- Sep-2007
- Publisher
- Springer Verlag
- Citation
- 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
- Indexed
- SCOPUS
- Journal Title
- Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
- Volume
- 4692 LNAI
- Number
- PART 1
- Start Page
- 379
- End Page
- 389
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/179579
- DOI
- 10.1007/978-3-540-74819-9_47
- ISSN
- 0302-9743
- 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.
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