Trajectory similarity clustering based on multi-feature distance measurement

Vehicle trajectory-clustering method based on road-network-sensitive features

Existing trajectory-clustering methods do not consider road-network connectivity, road directionality, and real path length while measuring the similarity between different road-network trajectories. This paper proposes a trajectory-clustering method based on road-network-sensitive features, which can solve the problem of similarity metrics among trajectories in the road network, and effectively combine their local and overall similarity features. First, the method performs the primary clustering of trajectories based on the overall vehicle motion trends. Then, the map-matched trajectories are clustered based on the road segment density, connectivity, and corner characteristics. Finally, clustering is then merged based on the multi-area similarity measure. The visualization and experimental results on real road-network trajectories show that the proposed method is more effective and comprehensive than existing methods, and more suitable for urban road planning, public transportation planning, and congested road detection.

A Novel Approach for Mining Time and Space Proximity-based Frequent Sequential Patterns from Trajectory Data

Trajectory Data have been considered as a treasure for various hidden patterns which provide deeper understanding of the underlying moving objects. Several studies are focused to extract repetitive, frequent and group patterns. Conventional algorithms defined for Sequential Patterns Mining problems are not directly applicable for trajectory data. Space Partitioning strategies were proposed to capture space proximity first and then time proximity to discover the knowledge in the data. Our proposal addresses time proximity first by identifying trajectories which meet at a minimum of [Formula: see text] time stamps in sequence. A novel tree structure is proposed to ease the process. Our method investigates space proximity using Mahalanobis distance (MD). We have used the Manhattan distance to form prior knowledge that helps the supervised learning-based MD to derive the clusters of trajectories along the true spreads of the objects. With the help of minsup threshold, clusters of frequent trajectories are found and then in sequence they form [Formula: see text] length Sequential Patterns. Illustrative examples are provided to compare the MD metric with Euclidean distance metric, Synthetic dataset is generated and results are presented considering the various parameters such as number of objects, minsup, [Formula: see text] value, number of hops in any trajectory and computational time. Experiments are done on available real-time dataset, taxi dataset, too. Sequential Patterns are proved to be worthy of knowledge to understand dynamics of the moving objects and to recommend the movements in constrained networks.