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Integrating Simulation and Signal Processing with Stochastic Social Kinetic Model

  • Fan Yang
  • Wen DongEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10354)

Abstract

Data that continuously track the dynamics of large populations have spurred a surge in research on computational sustainability. 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 streams. This integration enables researchers to specify and track the dynamics of 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 experimental 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.

Keywords

Ground Truth Particle Filter Recurrent Neural Network Discrete Event Simulator Dynamic Bayesian Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Blondel, V.D., Decuyper, A., Krings, G.: A survey of results on mobile phone datasets analysis (2015). arXiv preprint: arXiv:1502.03406
  2. 2.
    Borshchev, A.: The Big Book of Simulation Modeling: Multimethod Modeling with AnyLogic 6. AnyLogic North America, Chicago (2013)Google Scholar
  3. 3.
    Boyen, X.: Inference and learning in complex stochastic processes. Ph.D. thesis, Stanford University (2002)Google Scholar
  4. 4.
    Castellano, C., Fortunato, S., Loreto, V.: Statistical physics of social dynamics. Rev. Mod. Phys. 81(2), 591–646 (2009)CrossRefGoogle Scholar
  5. 5.
    Dong, W., Heller, K., Pentland, A.S.: Modeling infection with multi-agent dynamics. In: Yang, S.J., Greenberg, A.M., Endsley, M. (eds.) SBP 2012. LNCS, vol. 7227, pp. 172–179. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-29047-3_21 CrossRefGoogle Scholar
  6. 6.
    Forrester, J.W.: Industrial Dynamics. MIT Press, Cambridge (1961)Google Scholar
  7. 7.
    Gillespie, D.T.: Stochastic simulation of chemical kinetics. Annu. Rev. Phys. Chem. 58, 35–55 (2007)CrossRefGoogle Scholar
  8. 8.
    Goldenberg, A., Zheng, A.X., Fienberg, S.E., Airoldi, E.M.: A survey of statistical network models. Found. Trends® Mach. Learn. 2(2), 129–233 (2010)CrossRefzbMATHGoogle Scholar
  9. 9.
    Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)Google Scholar
  10. 10.
    Goss, P.J., Peccoud, J.: Quantitative modeling of stochastic systems in molecular biology by using stochastic petri nets. Proc. Natl. Acad. Sci. 95(12), 6750–6755 (1998)CrossRefGoogle Scholar
  11. 11.
    Grassmann, W.K.: Transient solutions in Markovian queueing systems. Comput. Oper. Res. 4, 47–53 (1977)CrossRefGoogle Scholar
  12. 12.
    Guan, T., Dong, W., Koutsonikolas, D., Qiao, C.: Fine-grained location extraction and prediction with little known data. In: Proceedings of the 2017 IEEE Wireless Communications and Networking Conference. IEEE Communications Society (2017)Google Scholar
  13. 13.
    Marsan, M.A., Balbo, G., Conte, G., Donatelli, S., Franceschinis, G.: Modelling with Generalized Stochastic Petri Nets. Wiley, New York (1994)zbMATHGoogle Scholar
  14. 14.
    MATSim Development Team (eds.): MATSIM-T: aims, approach and implementation. Technical report, IVT, ETH Zürich, Zürich (2007)Google Scholar
  15. 15.
    de Montjoye, Y.A., Smoreda, Z., Trinquart, R., Ziemlicki, C., Blondel, V.D.: D4D-Senegal: the second mobile phone data for development challenge (2014). arXiv preprint: arXiv:1407.4885
  16. 16.
    Murphy, K.P.: Dynamic Bayesian networks: representation, inference and learning. Ph.D. thesis, University of California, Berkeley (2002)Google Scholar
  17. 17.
    Smith, G.L., Schmidt, S.F., McGee, L.A.: Application of statistical filter theory to the optimal estimation of position and velocity on board a circumlunar vehicle. National Aeronautics and Space Administration (1962)Google Scholar
  18. 18.
    Toussaint, M., Storkey, A.: Probabilistic inference for solving discrete and continuous state Markov decision processes. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 945–952. ACM (2006)Google Scholar
  19. 19.
    Wilkinson, D.J.: Stochastic Modelling for Systems Biology. CRC Press, Boca Raton (2011)zbMATHGoogle Scholar
  20. 20.
    Xu, Z., Dong, W., Srihari, S.N.: Using social dynamics to make individual predictions: variational inference with stochastic kinetic model. In: Advances in Neural Information Processing Systems, pp. 2775–2783 (2016)Google Scholar
  21. 21.
    Ziemke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transp. Res. Rec. J. Transp. Res. Board 2493, 117–125 (2015)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringState University of New York at BuffaloBuffaloUSA

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