Using Data Assimilation Method to Predict People Flow in Areas of Incomplete Data Availability

People flow dynamics from the basis of various applications such as traffic flow analysis, surveillance, business building security, and crowd motion prediction. With the development of sensing technology, diverse sensors have accumulated sufficient data for a proper understanding of pedestrian movement. As the amount of data increases, the automatic acquisition of crowd behavior and walking information has become more imperative. However, despite large developments in sensing technology, detecting and tracking pedestrians over a relatively large area is costly and high maintenance. In contrast to traditional individual-based analysis, we consider the movement of pedestrians as a whole entity by incorporating dynamic continuum flow theory and demonstrating how it is applied in our kernel-function-based model of people flow density. In order to reconstruct people flow in areas that are partially invisible to sensors, we assess data assimilation methods to predict the whole areas people flow. The experiments which involve 1D/2D simulation and real tracking data demonstrate the validity of our proposed method. Experimental results of real tracking data show that the estimated density in the invisible area is acceptably close to the true value.