利用数据同化方法预测数据不完全可用地区的人口流动

Yongwei Xu, R. Shibasaki, Xiaowei Shao
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引用次数: 0

摘要

人流动力学的基础从各种应用,如交通流量分析,监控,商业楼宇安全,人群运动预测。随着传感技术的发展,各种传感器已经积累了足够的数据,可以正确地理解行人的运动。随着数据量的增加,自动获取人群行为和行走信息变得更加迫切。然而,尽管传感技术有了很大的发展,但在相对较大的区域内检测和跟踪行人的成本很高,维护费用也很高。与传统的基于个体的分析不同,我们将动态连续流理论作为一个整体来考虑行人的运动,并展示了如何将其应用于基于核函数的人流密度模型中。为了重建部分传感器不可见区域的人流量,我们评估了数据同化方法来预测整个区域的人流量。通过一维/二维仿真和实际跟踪数据验证了该方法的有效性。实际跟踪数据的实验结果表明,估计的不可见区域的密度与真实值接近。
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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.
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