A Modeling Framework of Cyber-Physical-Social Systems with Human Behavior Classification Based on Machine Learning

  • Dongdong AnEmail author
  • Jing LiuEmail author
  • Xiaohong ChenEmail author
  • Tengfei LiEmail author
  • Ling Yin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11852)


Cyber-Physical-Social Systems (CPSS) is an emerging complicated topic in recent years which focuses on the researches of a combination of cyberspace, physical space and social space. Different from traditional Cyber-Physical-Systems, CPSS contain human who interacts with the cyber and physical part more frequently. So how to capture and analyse human behaviors play a vital role in CPSS performance evaluation. To improve the analysis accuracy of CPSS, the paper proposes a new modelling framework – stohMCharts (stochastic hybrid MARTE statecharts) which is an extension of MARTE statecharts for stochastic hybrid system modelling and analysis. Compared to MARTE statechart, in stohMCharts, we can model the CPSS in a unified way. Also, we associate stohMCharts to NSHA (Networks Stochastic Hybrid Automata) and use statistical model checker UPPAAL-SMC to verify the stohMCharts. We apply an autonomous car as an example to explain the efficiency of our proposed approaches.


Statistical model checking Cyber-Physical-Social Systems Stochastic hybrid MARTE statecharts Stochastic Hybrid Automata 


  1. 1.
    Chen, W., et al.: A novel ensemble approach of bivariate statistical-based logistic model tree classifier for landslide susceptibility assessment. Geocarto Int. 33(12), 1398–1420 (2018)CrossRefGoogle Scholar
  2. 2.
    David, A., Larsen, K.G., Legay, A., Poulsen, D.B.: Statistical model checking of dynamic networks of stochastic hybrid automata. Electr. Commun. EASST 66 (2014)Google Scholar
  3. 3.
    Dogru, N., Subasi, A.: Traffic accident detection using random forest classifier. In: 2018 15th Learning and Technology Conference (L&T), pp. 40–45. IEEE (2018)Google Scholar
  4. 4.
    Li, T., Li, J., Liu, Z., Li, P., Jia, C.: Differentially private Naive Bayes learning over multiple data sources. Inf. Sci. 444, 89–104 (2018)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Noi, P.T., Kappas, M.: Comparison of random forest, k-nearest neighbor, and support vector machine classifiers for land cover classification using sentinel-2 imagery. Sensors 18(1), 18 (2018)Google Scholar
  6. 6.
    Xu, J., Xu, C., Zou, B., Tang, Y.Y., Peng, J., You, X.: New incremental learning algorithm with support vector machines. IEEE Trans. Syst. Man Cybern. Syst. (2018) Google Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Computer Science and Software EngineeringEast China Normal UniversityShanghaiChina
  2. 2.Shanghai University of Engineering ScienceShanghaiChina

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