Computing and Information Systems - Theses
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ItemUser centric cellular trajectory inference with partial knowledge( 2014)The uncertainty associated with cellular network trajectories is a major problem for their use in location based applications such as person tracking. Inferring the real trajectory of a person, using highly imprecise cellular trajectory data, is a challenging task. GPS trajectories are subjected to less uncertainty compared to cellular network trajectories and are preferred over cellular network trajectories, for many location based applications. However, GPS based location acquisition has limited applicability for certain contexts due to high power consumption and poor coverage. Cellular network based location acquisition is therefore a good alternative for GPS in such scenarios. Consequently, a cellular trajectory inference method which can handle the uncertainty of cellular trajectories is promising to investigate, such that cellular trajectories can be utilised in location based applications. In this thesis, our main focus is on user centric trajectory inference approaches, where the trajectory inference is performed by mobile phone users rather than the mobile network operator. Many existing cellular trajectory inference methods use knowledge about the cellular network such as the spatial distribution of neighbouring cell towers and signal strength information. However, this full knowledge about the cellular network is confidential to the mobile network operator, and mobile phone users are not guaranteed to have access to such information. Therefore, these techniques are not applicable for user centric cellular trajectory inference with partial knowledge about the cellular network. Therefore, user centric approaches for cellular trajectory inference are even more challenging. We propose a cellular trajectory inference method which utilises only a user’s connected cell tower location sequence and corresponding timing information, as this is the only type of knowledge guaranteed for a mobile phone user. We suggest using a user’s speed information as background knowledge, as it is easily accessible by the user, compared to knowledge about the cellular network. Furthermore, we suggest exploiting the preciseness of the time dimension of cellular trajectories to obtain precise handover times. These precise handover times can be used with speed information to accurately compute the distance, a user has travelled within a cell. We propose a method to infer the straight line segments of a trajectory, using above distance information. The inferred straight line trajectory segments are later used to estimate other segments of the trajectory. We theoretically and experimentally show that our proposed method achieves higher accuracy than existing cellular trajectory inference methods, for cases where the user’s trajectory tends to be a combination of straight lines. The intuition behind straight line inference is that people follow the shortest path to reach a destination avoiding unnecessary turns and therefore often prefer to select a straight line path. Additional advantages of our proposed inference method include the ability to locally run on mobile devices and the ability to perform trajectory inference within an unfamiliar environment, since no historical trajectory information or pre-training is required.