Extracting Movement Patterns Using Fuzzy and Neuro-fuzzy Approaches

Several applications generate large volumes of data on movements including vehicle navigation, fleet management, wildlife tracking and in the near future cell phone tracking. Such applications require support to manage the growing volumes of movement data. Understanding how an object moves in space and time is fundamental to the development of an appropriate movement model of the object. Many objects are dynamic and their positions change with time. The ability to reason about the changing positions of moving objects over time thus becomes crucial. Explanations on movements of an object require descriptions of the patterns they exhibit over space and time. Every moving object exhibits a wide range of patterns some of which repeat but not exactly over space and time such as an animal foraging or a delivery truck moving about a city. Even though movement patterns are not exactly the same, they are not completely different. Moving objects may move on the same or nearly similar paths and visit the same locations over time. This thesis addresses the identification of repeat movement patterns from large volumes of data. These are represented as higher-level movement structures referred to as movement signatures. Movement signatures are defined as collections of patterns that objects demonstrate in their sequences of movements. Signatures have a spatial structure that includes dominant or frequently visited locations and paths and a spatio-temporal structure that associates a temporal pattern with the spatial patterns. This thesis demonstrates the extraction of movement signatures from sets of movement observations using fuzzy and Neuro-fuzzy methodologies. Identification of movement signatures and definition of their attributes provides summary level information for modeling and reasoning about moving objects