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Integrating simulation and signal processing in tracking complex social systems

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Abstract

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.

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Notes

  1. Code and data are available at https://goo.gl/vOu8HH.

  2. A video animation is available at https://goo.gl/OLmDYW. Two web-based animations are available at https://goo.gl/8qkC7h; https://goo.gl/dGvFbz. Click Play button at the upper left corner to begin the animation.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, University at Buffalo, Buffalo, NY, USA

    Fan Yang & Wen Dong

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  1. Fan Yang
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  2. Wen Dong
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Correspondence to Wen Dong.

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Yang, F., Dong, W. Integrating simulation and signal processing in tracking complex social systems. Comput Math Organ Theory 26, 1–22 (2020). https://doi.org/10.1007/s10588-018-9276-6

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  • DOI: https://doi.org/10.1007/s10588-018-9276-6

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