A Behavior-Based Human Movement Simulator

Most location data simulators only generate raw movement data, which are often unlabeled and do not follow realistic behaviors or patterns of movement usually displayed by real-life objects. This work implements a georeferenced data simulator that generates spatial and temporal data instances following a parameterizable behavioral profile. The simulator generates both expected movements and deviations (or anomalies) to mimic real-life movement patterns and adds noise to mimic real-life limitations in tracking devices. A base simulation pipeline is implemented, which generates anomaly-free data points based on a given model. Behavior-based movement data are modeled with a Markov chain, defining the states as significant places (or POI) that the simulated individual can visit. Behavior is modeled as patterns in the significant places and the movements between them. An additional layer of simulation implements abnormalities (anomalies) in the data based on probabilities given by the user of the simulator. The anomalies are simulated using standard statistical techniques, considering the distributions that model the expected behavioral patterns. The simulator is implemented and evaluated by analyzing the spatial and temporal patterns of the defined POI in the data and the movements between them. It is concluded that the proposed simulator generates realistic data and can be applied as a useful tool to overcome the limitations found in other data generation alternatives.