Spatiotemporal mobility data has significant role and impact on the global economy and our everyday lives. The improvements along the last decades in terms of data management, planning of operations, security of operations, information provision to operators and end-users, has been driven by location-centred information. While a shift of paradigm regarding mobility data towards trajectory-oriented tasks is emerging, the ever-increasing volume of data emphasises the need for advanced methods supporting detection and prediction of events and trajectories, supplemented by advanced visual analytic methods, over multiple heterogeneous, voluminous, fluctuating, and noisy data streams of moving entities.

This book provides a comprehensive and detailed description of Big Data solutions towards activity detection and forecasting in very large numbers of moving entities spread across large geographical areas. Specifically, following a trajectory oriented approach, this book reports on state of the art methods for the detection and prediction of trajectories and important events related to moving entities, together with advanced visual analytics methods, over multiple heterogeneous, voluminous, fluctuating, and noisy data streams from moving entities, correlating them with data from archived data sources expressing, among others, entities’ characteristics, geographical information, mobility patterns, regulations and intentional data (e.g. planned routes), in a timely manner. Solutions provided are motivated, validated and evaluated in user-defined challenges focusing on increasing the safety, efficiency and economy of operations concerning moving entities in the Air-Traffic Management and Maritime domains.

More details can be found here.