Scientists study trajectory data to understand trends in movement patterns, such as human mobility for traffic analysis and urban planning. There is a pressing need for scalable and efficient techniques for analyzing this data and discovering the underlying patterns. In this paper, we introduce a novel technique which we call vector-field @math -means. The central idea of our approach is to use vector fields to induce a similarity notion between trajectories. Other clustering algorithms seek a representative trajectory that best describes each cluster, much like @math -means identifies a representative "center" for each cluster. Vector-field @math -means, on the other hand, recognizes that in all but the simplest examples, no single trajectory adequately describes a cluster. Our approach is based on the premise that movement trends in trajectory data can be modeled as flows within multiple vector fields, and the vector field itself is what defines each of the clusters. We also show how vector-field @math -means connects techniques for scalar field design on meshes and @math -means clustering. We present an algorithm that finds a locally optimal clustering of trajectories into vector fields, and demonstrate how vector-field @math -means can be used to mine patterns from trajectory data. We present experimental evidence of its effectiveness and efficiency using several datasets, including historical hurricane data, GPS tracks of people and vehicles, and anonymous call records from a large phone company. We compare our results to previous trajectory clustering techniques, and find that our algorithm performs faster in practice than the current state-of-the-art in trajectory clustering, in some examples by a large margin.
Liu et al. @cite_9 also present a visual analytics system for exploring route diversity within a city, based on thousands of taxi trajectories. Their system offers global views of all trajectories, but also drills down to routes between source destination pairs, and even to specific road segments. Their work is more about examining trajectories and less about clustering them.
@cite_9
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Route suggestion is an important feature of GPS navigation systems. Recently, Microsoft T-drive has been enabled to suggest routes chosen by experienced taxi drivers for given source destination pairs in given time periods, which often take less time than the routes calculated according to distance. However, in real environments, taxi drivers may use different routes to reach the same destination, which we call route diversity. In this paper we first propose a trajectory visualization method that examines the regions where the diversity exists and then develop several novel visualization techniques to display the high dimensional attributes and statistics associated with different routes to help users analyze diversity patterns. Our techniques have been applied to the real trajectory data of thousands of taxis and some interesting findings about route diversity have been obtained. We further demonstrate that our system can be used not only to suggest better routes for drivers but also to analyze traffic bottlenecks for transportation management.