Emergency-Oriented Spatiotemporal Trajectory Pattern Recognition by Intelligent Sensor Devices
This paper presents an emergency-oriented procedure to recognize trajectory patterns by analyzing GPS data collected from intelligent sensor devices. An overall description, including design architecture and system modules, is presented. The primary issues are devoted to satisfying the requirements of key group identication and surveillance under normal and emergency circumstance. For the sake of panoramic understanding of human distribution and movement, semantic trajectory information is extracted from dynamic transportation data and static human distribution data. The sequential Monte Carlo method in conjunction with a state-transition model is employed to predict the updating real-time locations. The proposed algorithm selects particles from time-stamped sequential historical data sets. Simultaneously, a resampling strategy is developed to replace low-weight particles. A curve similarity measurement called Fréchet distance is employed to compare trajectories and city roads. Afterward, human daily location and signicant locations are identied based on the clustering method. To evaluate the proposed procedure and methods, sequential trajectory data sets come from the GeoLife project along with human distribution logs from smartphone application EMAPP are utilized. Finally, we demonstrate the potential of dealing location information for promoting emergency management.
Emergency management, human distribution, moving pattern recognition, spatiotemporal trajectories, trajectory data mining.