A Spatio-Temporal Simulation Model for Incremental Clustering in Massive Moving Objects Data Set

The real-world data process of large spatio-temporal data collection presents a very difficult technical problem. Firstly, the given process is very expensive, requiring a lot of various high-technology software instruments and modern hardware infrastructure (sensors, servers, GPS infrastructure etc.) installations; secondly, this process sometimes cannot show special traffic patterns, which we may characterize as patterned traffic trajectories. The Arena simulation framework introduced in this paper uses our suggested random linear interpolation algorithm and spatio-temporal prediction algorithm, which are applicable to visualize, handle and predict movement data with various time resolutions.