Data that continuously track the dynamics of large populations have the potential to revolutionize how we study complex social systems. However, coping with massive, noisy, unstructured, and disparate data streams is not easy. In this paper, we describe a particle filter algorithm that integrates signal processing and simulation modeling to study complex social systems using massive, noisy, unstructured data. This integration enables researchers to specify and track the dynamics of real-world complex social systems by building a simulation model. To show the effectiveness of this algorithm, we infer city-scale traffic dynamics from the observed trajectories of a small number of probe vehicles uniformly sampled from the system. The results show that our model can not only track and predict human mobility, but also explain how traffic is generated through the movements of individual vehicles. The algorithm and its application point to a new way of bringing together modelers and data miners to turn the real world into a living lab.

中文翻译：

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将模拟和信号处理集成在跟踪复杂的社会系统中

持续跟踪大量人口动态的数据有可能彻底改变我们研究复杂社会系统的方式。但是，要处理大量，嘈杂，非结构化和分散的数据流并不容易。在本文中，我们描述了一种粒子滤波算法，该算法集成了信号处理和仿真模型，以使用大量，嘈杂的非结构化数据来研究复杂的社会系统。这种集成使研究人员可以通过构建仿真模型来指定和跟踪现实世界中复杂的社会系统的动态。为了显示该算法的有效性，我们从从系统中统一采样的少量探测车的观测轨迹推断出城市规模的交通动态。结果表明，我们的模型不仅可以跟踪和预测人员流动性，而且还说明了如何通过各个车辆的行驶产生交通。该算法及其应用指出了一种将建模人员和数据挖掘人员聚集在一起的新方法，可以将现实世界变成一个有生命的实验室。