Abstract
Clustering trajectories from GPS data is a crucial task for developing applications in intelligent transportation systems. Most existing approaches perform clustering on raw data consisting of series of GPS positions of moving objects over time. Such approaches are not suitable for classifying mov
Highlights
With the ever-increasing number of smartphones and mobile devices equipped with Global Positioning Systems (GPS) receivers, large amounts of spatio-temporal data can be collected from moving objects, e.g., vehicles or travellers
Cluster 4 contains trajectories of vehicles passing through Ho Chi Minh City
The results from the experiments shows that the histogram-based feature extraction can be employed for discovering meaningful clusters from raw GPS trajectories
Summary
With the ever-increasing number of smartphones and mobile devices equipped with Global Positioning Systems (GPS) receivers, large amounts of spatio-temporal data can be collected from moving objects, e.g., vehicles or travellers. Such massive quantity of data has led to a rise in the number of data mining tasks aiming to analyse and discover useful information for real life applications [1]. Most of existing approaches to tackle the problem of finding similar trajectories is to apply traditional clustering algorithms on raw trajectory data Such kinds of approaches miss similar partitions shared by trajectories which belong to different groups [4]. EAI Endorsed Transactions on Before describing the proposed approach in details, we first review the related work and define some concepts to formulate the problem
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