Dense traffic flow patterns mining in bi-directional road networks using density based trajectory clustering

Dense traffic flow patterns mining in bi-directional road networks using density based trajectory... Due to the rapid growth of wireless communications and positioning technologies, trajectory data have become increasingly popular, posing great challenges to the researchers of data mining and machine learning community. Trajectory data are obtained using GPS devices that capture the position of an object at specific time intervals. These enormous amounts of data necessitates to explore efficient and effective techniques to extract useful information to solve real world problems. Traffic flow pattern mining is one of the challenging issues for many applications. In a literature significant number of approaches are available to cluster the trajectory data, however the clustering has not been explored for trajectories pattern mining in bi-directional road networks. This paper presents a novel technique for excavating heavy traffic flow patterns in bi-directional road network, i.e. identifying divisions of the roads where the traffic flow is very dense. The proposed technique works in two phases: phase I, finds the clusters of trajectory points based on density of trajectory points; phase II, arranges the clusters in sequence based on spatiotemporal values for each route and directions. These sequences represent the traffic flow patterns. All the routes and sections exceeding a user specified minimum traffic threshold are marked as high dense traffic areas. The experiments are performed on synthetic dataset. The proposed algorithm efficiently and accurately finds the dense traffic in bi-directional roads. Proposed clustering method is compared with the standard k-means clustering algorithm for the performance evaluation. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Advances in Data Analysis and Classification Springer Journals

Dense traffic flow patterns mining in bi-directional road networks using density based trajectory clustering

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Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2016 by Springer-Verlag Berlin Heidelberg
Subject
Statistics; Statistical Theory and Methods; Data Mining and Knowledge Discovery
ISSN
1862-5347
eISSN
1862-5355
D.O.I.
10.1007/s11634-016-0256-8
Publisher site
See Article on Publisher Site

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