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Data-Driven Agent-Based Simulation for Pedestrian Capacity Analysis

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10861)

Abstract

In this paper, an agent-based data-driven model that focuses on path planning layer of origin/destination popularities and route choice is developed. This model improves on the existing mathematical modeling and pattern recognition approaches. The paths and origins/destinations are extracted from a video. The parameters are calibrated from density map generated from the video. We carried out validation on the path probabilities and densities, and showed that our model generates better results than the previous approaches. To demonstrate the usefulness of the approach, we also carried out a case study on capacity analysis of a building layout based on video data.

Notes

Acknowledgement

Singkuang Tan, Nan Hu, and Wentong Cai would like to acknowledge the support from the grant: IHPC-NTU Joint R&D Project on “Symbiotic Simulation and Video Analysis of Crowds”.

References

  1. 1.
    Asakura, Y., Hato, E., Kashiwadani, M.: Origin-destination matrices estimation model using automatic vehicle identification data and its application to the Han-Shin expressway network. Transportation 27(4), 419–438 (2000)CrossRefGoogle Scholar
  2. 2.
    Charalambous, P., Karamouzas, I., Guy, S.J., Chrysanthou, Y.: A data-driven framework for visual crowd analysis. In: CGF, vol. 33, pp. 41–50. Wiley Online Library (2014)CrossRefGoogle Scholar
  3. 3.
    Das, S., Suganthan, P.N.: Differential evolution: a survey of the state-of-the-art. TEVC 15(1), 4–31 (2011)Google Scholar
  4. 4.
    Fiorini, P., Shiller, Z.: Motion planning in dynamic environments using velocity obstacles. IJRR 17(7), 760–772 (1998)Google Scholar
  5. 5.
    Fruin, J.J.: Pedestrian planning and design. Technical report (1971)Google Scholar
  6. 6.
    Guy, S.J., Van Den Berg, J., Liu, W., Lau, R., Lin, M.C., Manocha, D.: A statistical similarity measure for aggregate crowd dynamics. TOG 31(6), 190 (2012)CrossRefGoogle Scholar
  7. 7.
    Helbing, D., Molnár, P.: Social force model for pedestrian dynamics. Phys. Rev. E 51, 4282–4286 (1995)CrossRefGoogle Scholar
  8. 8.
    Molyneaux, N., Scarinci, R., Bierlaire, M.: Pedestrian management strategies for improving flow dynamics in transportation hubs. In: STRC (2017)Google Scholar
  9. 9.
    Prato, C.G.: Route choice modeling: past, present and future research directions. J. Choice Model. 2(1), 65–100 (2009).  https://doi.org/10.1016/S1755-5345(13)70005-8. http://www.sciencedirect.com/science/article/pii/S1755534513700058CrossRefGoogle Scholar
  10. 10.
    Rao, A.M., Rao, K.R.: Measuring urban traffic congestion-a review. IJTTE 2(4) (2012)Google Scholar
  11. 11.
    Shi, J., Tomasi, C.: Good features to track. In: CVPR, pp. 593–600 (1994).  https://doi.org/10.1109/CVPR.1994.323794
  12. 12.
    Tan, S.K.: Visual detection and crowd density modeling of pedestrians. Ph.D. thesis, SCSE, NTU (2017). http://hdl.handle.net/10356/72746
  13. 13.
    Vanumu, L.D., Rao, K.R., Tiwari, G.: Fundamental diagrams of pedestrian flow characteristics: a review. ETRR 9(4), 49 (2017)Google Scholar
  14. 14.
    Wang, H., Ondřej, J., O’Sullivan, C.: Trending paths: a new semantic-level metric for comparing simulated and real crowd data. TVCG 23(5), 1454–1464 (2017)Google Scholar
  15. 15.
    Wang, H., O’Sullivan, C.: Globally continuous and non-Markovian crowd activity analysis from videos. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9909, pp. 527–544. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46454-1_32CrossRefGoogle Scholar
  16. 16.
    Wang, H., Yu, L., Qin, S.: Simulation and optimization of passenger flow line in Lanzhou West Railway Station. In: Sierpiński, G. (ed.) TSTP 2017. Advances in Intelligent Systems and Computing, vol. 631, pp. 61–73. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-62316-0_5CrossRefGoogle Scholar
  17. 17.
    Wang, R., Zhang, Y., Yue, H.: Developing a new design method avoiding latent congestion danger in urban rail transit station. Transp. Res. Procedia 25, 4083–4099 (2017)Google Scholar
  18. 18.
    Wolinski, D., J Guy, S., Olivier, A.H., Lin, M., Manocha, D., Pettré, J.: Parameter estimation and comparative evaluation of crowd simulations. In: CGF, vol. 33, pp. 303–312. Wiley Online Library (2014)CrossRefGoogle Scholar
  19. 19.
    Zhao, M., Turner, S.J., Cai, W.: A data-driven crowd simulation model based on clustering and classification. In: DS-RT, pp. 125–134. IEEE (2013)Google Scholar
  20. 20.
    Zhong, J., Cai, W., Lees, M., Luo, L.: Automatic model construction for the behavior of human crowds. Appl. Soft Comput. 56, 368–378 (2017).  https://doi.org/10.1016/j.asoc.2017.03.020CrossRefGoogle Scholar
  21. 21.
    Zhong, J., Cai, W., Luo, L., Yin, H.: Learning behavior patterns from video: a data-driven framework for agent-based crowd modeling. In: AAMAS, pp. 801–809 (2015). http://dl.acm.org/citation.cfm?id=2773256
  22. 22.
    Zhong, J., Hu, N., Cai, W., Lees, M., Luo, L.: Density-based evolutionary framework for crowd model calibration. J. Comput. Sci. 6, 11–22 (2015)CrossRefGoogle Scholar
  23. 23.
    Zhou, B., Wang, X., Tang, X.: Understanding collective crowd behaviors: learning a mixture model of dynamic pedestrian-agents. In: CVPR, pp. 2871–2878. IEEE (2012)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computer Science and EngineeringNanyang Technological UniversitySingaporeSingapore
  2. 2.Institution of High Performance ComputingAgency for Science Technology and ResearchSingaporeSingapore

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