الفهرس | Only 14 pages are availabe for public view |
Abstract There has been a tremendous growth in movement data due to availability of devices that could be used to track movement of objects. Tracking an object gives rise to a sequence of points in time and space, called a trajectory. One of the main functions is evaluating similarity between moving objects’ trajectories and it has gained much attention in many application domains. There exist similarity measures in the literature that propose evaluating similarity between trajectories in the form of time stamped values, denes some meaning of similarity and propose algorithms for computing it. The user is restricted to that meaning of similarity while it should be application dependent and only determined by the user. Therefore, there is a lack of genericness where there is a need for a generic approach where users can dene the meaning of similarity. In this thesis, a new parametrized similarity operator, TWEDistance, is proposed. This operator is based on one of the discrete similarity measures, time warp edit distance, where the meaning of similarity is generic and left for user to dene and it is implemented in Secondo. The similarity measures in the literature that are based on the discrete form of a trajectory is also aected by the sampling rate differences as it is dened over sequences of points. Therefore,this thesis propose to deal with the nature of trajectory’s data as a continuous function. Continuous based similarity is evaluated using interpolation, regression and curve barcoding. In the experimental evaluation, rst, the accuracy of the TWEDis tance operator is evaluated based on dierent meaning of similarity and the results were as settled in the experiments settings with an accuracy up to 100%. Second, a comparative study is made between TWEDistance operator, regression, interpolation and curve barcoding while considering sampling rate dierences. The results showed that interpolation gives higher average accuracy up to 90%. Experiments were made over both real and synthetic datasets. 2. |