Advertisement

A CPS-Enhanced Subway Operations Safety System Based on the Short-Term Prediction of the Passenger Flow

  • Shaobo Zhong
  • Zhi Xiong
  • Guannan Yao
  • Wei Zhu
Chapter

Abstract

The subway transport system is an effective means to mitigate the adverse effects of rapid urbanization and traffic congestion. The development of subway systems has created great challenges to subway operations safety management, including precaution and response efforts. Accurate predictions, timely control, and feedback-based continuous analysis and dispatch are critical to developing subway systems. We created a CPS-enhanced subway operations safety framework using the concept of CPS and short-term prediction techniques of subway passenger flow; our framework is characterized by a “flexible and controllable, real-time operation” composed of six components: system, adjust, facilities, early warning, time control, and yielding. In the framework, the forecasting methods of subway passenger flow are the core, and cyber-physical systems are used to couple other components into a safety management information platform in which the CPS is responsible for sensing, control, and feedback in the entire operating process. The entire operating process includes the input acquisition for the forecasting models, early warning publishing and emergency control, and feedback-based re-analysis and dispatch. The proposed framework can provide integrated services for disaster prevention and the control of subway operations.

Keywords

Subway passenger flow Cyber-physical system Short-term prediction Safety operations management 

Notes

Acknowledgements

This paper was supported in part by the National Key R&D Program of China (No 2019YFF0301300) and the National Natural Science Foundation of China (Nos. 70901047 and 7177030217). We also appreciate support for this paper from the Beijing Key Laboratory of Operation Safety of Gas, Heating, and Underground Pipelines.

References

  1. 1.
    X. Ma, Z. Tao, Y. Wang, H. Yu, Y. Wang, Long short-term memory neural network for traffic speed prediction using remote microwave sensor data. Transp. Res. C Emerg. Technol. 54, 187–197 (2015)CrossRefGoogle Scholar
  2. 2.
    A.J. Horowitz, Simplifications for single-route transit-ridership forecasting models. Transportation 12(3), 261–275 (1984)CrossRefGoogle Scholar
  3. 3.
    S. Zhu, X. Luo, Z. Xu, L. Ye, Seasonal streamflow forecasts using mixture-kernel GPR and advanced methods of input variable selection. Hydrol. Res. 50(1), 200–214 (2019)CrossRefGoogle Scholar
  4. 4.
    L.D. Galicia, R.L. Cheu, Geographic information system–system dynamics procedure for bus rapid transit ridership estimation. J. Adv. Transp. 47(3), 266–280 (2013)CrossRefGoogle Scholar
  5. 5.
    K.T. Azar, J. Ferreira, Integrating geographic information systems into transit ridership forecast models. J. Adv. Transp. 29(3), 263–279 (1995)CrossRefGoogle Scholar
  6. 6.
    J. Zhao, W. Deng, Y. Song, Y. Zhu, Analysis of Metro ridership at station level and station-to-station level in Nanjing: an approach based on direct demand models. Transportation 41(1), 133–155 (2014)CrossRefGoogle Scholar
  7. 7.
    J. Zhao, W. Deng, Y. Song, Y. Zhu, What influences Metro station ridership in China? Insights from Nanjing. Cities 35, 114–124 (2013)Google Scholar
  8. 8.
    A.O. Idris, K.M. Nurul Habib, A. Shalaby, An investigation on the performances of mode shift models in transit ridership forecasting. Transp. Res. A Policy Prac. 78, 551–565 (2015)CrossRefGoogle Scholar
  9. 9.
    S. Chan, L. Miranda-Moreno, A station-level ridership model for the metro network in Montreal, Quebec. Can. J. Civ. Eng. 40(3), 254–262 (2013)CrossRefGoogle Scholar
  10. 10.
    B.D. Taylor, D. Miller, H. Iseki, C. Fink, Nature and/or nurture? Analyzing the determinants of transit ridership across US urbanized areas. Transp. Res. A Policy Prac. 43(1), 60–77 (2009)Google Scholar
  11. 11.
    Z. Fang, X. Yang, Y. Xu, S.-L. Shaw, L. Yin, Spatiotemporal model for assessing the stability of urban human convergence and divergence patterns. Int. J. Geogr. Inf. Sci. 31(11), 2119–2141 (2017)CrossRefGoogle Scholar
  12. 12.
    M.G. Karlaftis, E.I. Vlahogianni, Statistical methods versus neural networks in transportation research: differences, similarities and some insights. Transp. Res. C Emerg. Technol. 19(3), 387–399 (2011)CrossRefGoogle Scholar
  13. 13.
    R. Xue, D.J. Sun, S. Chen, Short-term bus passenger demand prediction based on time series model and interactive multiple model approach. Discret. Dyn. Nat. Soc. 2015 (2015). https://doi.org/10.1155/2015/682390
  14. 14.
    X. Ma, Y.-J. Wu, Y. Wang, F. Chen, J. Liu, Mining smart card data for transit riders’ travel patterns. Transp. Res. C Emerg. Technol. 36, 1–12 (2013)CrossRefGoogle Scholar
  15. 15.
    Z. Xiong, S. Zhong, D. Song, Z. Yu, Q. Huang, A method of fitting urban rail transit passenger flow time series. China Saf. Sci. J. 28(11), 39–45 (2018)Google Scholar
  16. 16.
    T.-H. Tsai, C.-K. Lee, C.-H. Wei, Neural network based temporal feature models for short-term railway passenger demand forecasting. Expert Syst. Appl. 36(2), 3728–3736 (2009)CrossRefGoogle Scholar
  17. 17.
    Z. Xiong, J. Zheng, D. Song, S. Zhong, Q. Huang, Passenger flow prediction of urban rail transit based on deep learning methods. Smart Cities 2(3), 371–387 (2019)CrossRefGoogle Scholar
  18. 18.
    S. Zhu, X. Yuan, Z. Xu, X. Luo, H. Zhang, Gaussian mixture model coupled recurrent neural networks for wind speed interval forecast. Energy Convers. Manag. 198, 111772 (2019)CrossRefGoogle Scholar
  19. 19.
    X. Wang, K. An, L. Tang, X. Chen, Short term prediction of freeway exiting volume based on SVM and KNN. Int. J. Transp. Sci. Technol. 4(3), 337–352 (2015)CrossRefGoogle Scholar
  20. 20.
    Y. Sun, B. Leng, W. Guan, A novel wavelet-SVM short-time passenger flow prediction in Beijing subway system. Neurocomputing 166, 109–121 (2015)CrossRefGoogle Scholar
  21. 21.
    X. Jiang, L. Zhang, X.M. Chen, Short-term forecasting of high-speed rail demand: a hybrid approach combining ensemble empirical mode decomposition and gray support vector machine with real-world applications in China. Transp. Res. C Emerg. Technol. 44, 110–127 (2014)CrossRefGoogle Scholar
  22. 22.
    J.D. Hamilton, Time Series Analysis, vol. 2 (Princeton University Press, Princeton, 1994)zbMATHGoogle Scholar
  23. 23.
    Z. Zhao, W. Chen, X. Wu, P.C. Chen, J. Liu, LSTM network: a deep learning approach for short-term traffic forecast. IET Intell. Transp. Syst. 11(2), 68–75 (2017)CrossRefGoogle Scholar
  24. 24.
    L. Liu, R.-C. Chen, A novel passenger flow prediction model using deep learning methods. Transp. Res. C Emerg. Technol. 84, 74–91 (2017)CrossRefGoogle Scholar
  25. 25.
    E.I. Vlahogianni, M.G. Karlaftis, J.C. Golias, Short-term traffic forecasting: where we are and where we’re going. Transp. Res. C Emerg. Technol. 43, 3–19 (2014)CrossRefGoogle Scholar
  26. 26.
    G.P. Zhang, Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50, 159–175 (2003)zbMATHCrossRefGoogle Scholar
  27. 27.
    E.S. Gardner Jr, Exponential smoothing: the state of the art—Part II. Int. J. Forecast. 22(4), 637–666 (2006)CrossRefGoogle Scholar
  28. 28.
    J. Li, J.-H. Cheng, J.-Y. Shi, F. Huang, Brief introduction of back propagation (BP) neural network algorithm and its improvement, in Advances in Computer Science and Information Engineering (Springer, Berlin, 2012), pp. 553–558Google Scholar
  29. 29.
    D.E. Rumelhart, G.E. Hinton, R.J. Williams, Learning representations by back-propagating errors. Nature 323(3), 533–536 (1986)zbMATHCrossRefGoogle Scholar
  30. 30.
    K.G. Sheela, S.N. Deepa, Review on methods to fix number of hidden neurons in neural networks. Math. Probl. Eng. 2013 (2013). https://doi.org/10.1155/2013/425740
  31. 31.
    R.S. Toqeer, N.S. Bayindir, Speed estimation of an induction motor using Elman neural network. Neurocomputing 55(3–4), 727–730 (2003)CrossRefGoogle Scholar
  32. 32.
    J.L. Elman, Finding structure in time. Cogn. Sci. 14, 179–211 (1990)CrossRefGoogle Scholar
  33. 33.
    H.-P. Lu, Z.-Y. Sun, W.-C. Qu, Big data-driven based real-time traffic flow state identification and prediction. Discret. Dyn. Nat. Soc. 2015 (2015). https://doi.org/10.1155/2015/284906
  34. 34.
    X. Chen, J.W. Meaker, F.B. Zhan, Agent-based modeling and analysis of hurricane evacuation procedures for the Florida keys. Nat. Hazards 38(3), 321 (2006)Google Scholar
  35. 35.
    F. Rui, Z. Zuo, L. Li, Using LSTM and GRU neural network methods for traffic flow prediction. Youth Academic Conference of Chinese Association of Automation, 2017Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Shaobo Zhong
    • 1
  • Zhi Xiong
    • 2
  • Guannan Yao
    • 2
  • Wei Zhu
    • 1
  1. 1.Beijing Research Center of Urban Systems EngineeringBeijingP.R. China
  2. 2.Department of Engineering PhysicsTsinghua UniversityBeijingP.R. China

Personalised recommendations