Spatial trajectory prediction using a matrix representation

Inertia has a moving object follow a path or trajectory that resists any change in its motion. Human travel patterns normally have the similar inertia feature. For example, the vehicles on a highway usually stay on the highway or people tend to walk towards a popular destination such as a mall or park. This research tries to predict a spatial trajectory based on the current and previous trajectories. Spatial trajectory prediction requires a complicated processing of trajectories (lists of locations) such as trajectory collection, storage, indexing, transmission, and matching. This research makes trajectory prediction simple and effective by using an innovative matrix representation for trajectories. At the same time, user privacy is fully protected because the matrix representation allows the trajectories to be predicted at the mobile clients instead of the servers. By using our method, trajectory processing becomes matrix processing, which is well documented and includes a variety of tools and methods. This research is useful and popular and is related to a couple of subjects such as mobile computing and security, location-based services, and human behavior recognition.