Capturing complex behaviour for predicting distant future trajectories

We put forth a system, to predict distant-future positions of multiple moving entities and index the forecasted trajectories, in order to answer predictive queries involving long time horizons. Today, the proliferation of mobile devices with GPS functionality and internet connectivity has led to a rapid development of location-based services, accounting for user mobility prediction as a key paradigm. Mobility prediction is already playing a major role in traffic management, urban planning and location-based advertising, which demand accurate and long time horizon forecasting of user movements. Existing prediction methodologies either use motion patterns or techniques based on frequently visited places for predicting the next move. However, when it comes to distant-future, human mobility is too complex to be represented by such statistical functions. Therefore, the existing techniques are not well suited to answer distant-future queries with a satisfactory level of accuracy. To tackle this problem, we introduce a novel spatial object, 'Representative Trajectory', which embodies the movements of users amongst their zones of interest. We propose means to empirically evaluate the quality of this object and dynamically adapt its extraction method based on user mobility behaviour. We rely on an inverted index to store the predicted trajectories that scales well with the number of moving entities. Our evaluation results show that the technique achieves more than 70% accurate predictions with the best extraction technique. This shows that longer query time horizons do not necessarily demand complex spatial indexing schemes, which have to be rebalanced as they grow and which is a constantly experienced problem while answering predictive queries.