TraPlan: An Effective Three-in-One Trajectory-Prediction Model in Transportation Networks
The existing approaches for trajectory prediction (TP) are primarily concerned with discovering frequent trajectory patterns (FTPs) from historical movement data. Moreover, most of these approaches work by using a linear TP model to depict the positions of objects, which does not lend itself to the complexities of most real-world applications. In this research, the authors propose a three-in-one TP model in road-constrained transportation networks called TraPlan. TraPlan contains three essential techniques: 1) constrained network R-tree (CNR-tree), which is a two-tiered dynamic index structure of moving objects based on transportation networks; 2) a region-of-interest (RoI) discovery algorithm is employed to partition a large number of trajectory points into distinct clusters; and 3) a FTP-tree-based TP approach, called FTP-mining, is proposed to discover FTPs to infer future locations of objects moving within RoIs. In order to evaluate the results of the proposed CNR-tree index structure, the authors conducted experiments on synthetically generated data sets taken from real-world transportation networks. The results show that the CNR-tree can reduce the time cost of index maintenance by an average gap of about 40% when compared with the traditional NDTR-tree, as well as reduce the time cost of trajectory queries. Moreover, compared with fixed network R-Tree (FNR-trees), the accuracy of range queries has shown an on average improvement of about 32%. Furthermore, the experimental results show that the TraPlan demonstrates accurate and efficient prediction of possible motion curves of objects in distinct trajectory data sets by over 80% on average. Finally, the authors evaluate these results and the performance of the TraPlan model in regard to TP by comparing it with other TP algorithms.
- Record URL:
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
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Supplemental Notes:
- Abstract reprinted with permission of IEEE.
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Authors:
- Qiao, Shaojie
- Han, Nan
- Zhu, William
- Gutierrez, Louis Alberto
- Publication Date: 2015-6
Language
- English
Media Info
- Media Type: Digital/other
- Features: Figures; References;
- Pagination: pp 1188-1198
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Serial:
- IEEE Transactions on Intelligent Transportation Systems
- Volume: 16
- Issue Number: 3
- Publisher: Institute of Electrical and Electronics Engineers (IEEE)
- ISSN: 1524-9050
- Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979
Subject/Index Terms
- TRT Terms: Algorithms; Data mining; Forecasting; Location based services; Object oriented databases; Trajectory; Trajectory control
- Subject Areas: Data and Information Technology; Highways; I72: Traffic and Transport Planning;
Filing Info
- Accession Number: 01574957
- Record Type: Publication
- Files: TLIB, TRIS
- Created Date: Aug 28 2015 2:01PM