As technology advances we encounter more available data on moving objects, thus increasing our ability to mine spatiotemporal data. We can use this data for learning moving objects behavior and for predicting their locations at future times according to the extracted movement patterns.In this paper we cluster trajectories of a mobile object and utilize the accepted cluster centroids as the object's movement patterns. We use the obtained movement patterns for predicting the object location at specific future times. We evaluate our prediction results using precision and recall measures. We also remove exceptional data points from the moving patterns by optimizing the value of an exceptions threshold.
As technology advances, detailed data on the position of moving objects, such as humans and vehicles is available. In order to discover groups of mobile objects that usually move in similar ways we propose an incremental clustering algorithm that clusters mobile objects according to similarity of their movement patterns. The proposed clustering algorithm uses a new, "data-amount-based" similarity measure between mobile trajectories. The clustering algorithm is evaluated on two spatio-temporal datasets using clustering validity measures.
The real-world process of generating a large spatio-temporal data collection presents a very difficult technical problem. First, this process is very expensive, requiring a lot of various high-technology software tools and modern hardware infrastructure (sensors, servers, GPS infrastructure etc.) installations; second, the recorded trajectories sometimes cannot represent any special traffic or movement patterns. The simulation framework introduced in this paper can generate diverse trajectory datasets based on predetermined movement patterns.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.