OpenPTDS Dataset: Pedestrian Trajectories in Crowded Scenarios

  • Xiao SongEmail author
  • Jinghan Sun
  • Jing Liu
  • Kai Chen
  • Hongnan Xie
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 946)


Pedestrian simulation is an important approach for engineers to evaluate the safety issues of metro buildings. Although there exist many works of pedestrian evacuation, it is still lacking of rich evacuation data to calibrate simulation models. To overcome this problem, we conducted several real-life pedestrian experiments and create a data set named OpenPTDS. Fundamental speed-density diagram is drawn to show its feasibility. To promote further research and applications, the source data is shared at


Pedestrian trajectory Dataset Speed-density diagram 


  1. 1.
    Koo, J., Kim, Y.S., Kim, B.-I., Christensen, K.M.: A comparative study of evacuation strategies for people with disabilities in high-rise building evacuation. Expert Syst. Appl. 40, 408–417 (2013)CrossRefGoogle Scholar
  2. 2.
    Tan, L., Hu, M., Lin, H.: Agent-based simulation of building evacuation: combining human behavior with predictable spatial accessibility in a fire emergency. Inf. Sci. 295, 53–66 (2015)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Zia, K., Farrahi, K., Riener, A., Ferscha, A.: An agent-based parallel geo-simulation of urban mobility during city-scale evacuation. Simul.: Trans. Soc. Model. Simul. Int. 89(10), 1184–1214 (2013)CrossRefGoogle Scholar
  4. 4.
    D’Orazio, M., Spalazzi, L., Quagliarini, E., Bernardini, G.: Agent-based model for earthquake pedestrians’ evacuation in urban outdoor scenarios: behavioral patterns definition and evacuation paths choice. Saf. Sci. 62, 450–465 (2014)CrossRefGoogle Scholar
  5. 5.
    Ribeiro, J., Almeida, J.E., Rossetti, R.J.F., Coelho, A., Coelho, A.L.: Using serious games to train evacuation behavior. Inf. Syst. Technol. (CISTI) 7, 1–6 (2012)Google Scholar
  6. 6.
    Wu, Y., Song, X., Gong, G.: Real-time load balancing scheduling algorithm for periodic simulation models. Simul. Model. Pract. Theory 52(1), 123–134 (2015)CrossRefGoogle Scholar
  7. 7.
    Song, X., Han, D., Sun, J., Zhang, Z.: A data-driven neural network approach to simulate pedestrian movement. Phys. A-Stat. Mech. Appl. 509(11), 827–844 (2018)CrossRefGoogle Scholar
  8. 8.
    Ma, L., Song, X., Ma, Y., et al.: Selfishness- and selflessness-based models of pedestrian room evacuation. Phys. A-Stat. Mech. Appl. 447(4), 455–466 (2016)Google Scholar
  9. 9.
    Song, X., Zhang, S., Qian, L.: Opinion dynamics in networked command and control organizations. Phys. A-Stat. Mech. Appl. 392(20), 5206–5217 (2013)CrossRefGoogle Scholar
  10. 10.
    Shi, W., Song, X., Ma, Y., Yang, C.: Impact of informal networks on opinion dynamics in hierarchically formal organization. Phys. A-Stat. Mech. Appl. 436(10), 916–924 (2015)Google Scholar
  11. 11.
    Song, X., Chai, X., Zhang, L.: Modeling framework for product lifecycle information. Simul. Model. Pract. Theory 18(8), 1080–1091 (2010)CrossRefGoogle Scholar
  12. 12.
    Robicquet, A., Sadeghian, A., Alahi, A., Savarese, S.: Learning social etiquette: human trajectory understanding in crowded scenes. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 549–565. Springer, Cham (2016). Scholar
  13. 13.
    Pellegrini, S., Ess, A., Schindler, K., et al.: You’ll never walk alone: modeling social behavior for multi-target tracking. In: IEEE International Conference on Computer Vision, pp. 261–268. IEEE (2009)Google Scholar
  14. 14.
    Lerner, A., Chrysanthou, Y., Lischinski, D.: Crowds by example. Comput. Graph. Forum 26(3), 655–664 (2007)CrossRefGoogle Scholar
  15. 15.
    Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Soft + hardwired attention: an LSTM framework for human trajectory prediction and abnormal event detection. ArXiv e-prints, February 2017Google Scholar
  16. 16.
    Nash, J.: The bargaining problem. Econometrica 18, 155–162 (1950)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Song, X., Zhang, S., Qian, L.: Opinion dynamics in networked command and control organizations. Phys. A-Stat. Mech. Appl. 392(20), 5206–5217 (2013)CrossRefGoogle Scholar
  18. 18.
    Wang, Y., Wang, J., Song, X., Han, L.: An efficient adaptive fuzzy switching weighted mean filter for salt-and-pepper noise removal. IEEE Sig. Process. Lett. 23(11), 1582–1586 (2016)CrossRefGoogle Scholar
  19. 19.
    Zhao, L., Thorpe, C.E.: Stereo- and neural network-based pedestrian detection. IEEE Trans. Intell. Transp. Syst. 1(3), 148–154 (2002)CrossRefGoogle Scholar
  20. 20.
    Pang, G.K.H., Takabashi, K., Yokota, T., et al.: Adaptive route selection for dynamic route guidance system based on fuzzy-neural approaches. IEEE Trans. Veh. Technol. 48(6), 2028–2041 (1999)CrossRefGoogle Scholar
  21. 21.
    Cheng, X., Wang, C.X., Ai, B., et al.: Envelope level crossing rate and average fade duration of nonisotropic vehicle-to-vehicle Ricean fading channels. IEEE Trans. Intell. Transp. Syst. 15(1), 62–72 (2014)CrossRefGoogle Scholar
  22. 22.
    Weidmann, U.: Transporttechnik der fussganger, in Transporttechnische Eigenschaften des Fussgangerverkehrs. In: Schriftenreihe des IVT Nr. 90, 2nd edn. ETH Zurich, Zurich, March 1993Google Scholar
  23. 23.
    Song, X., Zhang, Z., Peng, G., et al.: Effect of authority figures for pedestrian evacuation at metro stations. Phys. A-Stat. Mech. Appl. 465(1), 599–612 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Xiao Song
    • 1
    Email author
  • Jinghan Sun
    • 2
  • Jing Liu
    • 1
  • Kai Chen
    • 1
  • Hongnan Xie
    • 1
  1. 1.School of AutomationBeihang UniversityBeijingChina
  2. 2.School of Computer Science and TechnologyBeihang UniversityBeijingChina

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