Skip to main content
SpringerLink
Log in

Real-time micro-modelling of city evacuations

  • Published:
Computational Particle Mechanics Aims and scope Submit manuscript

Abstract

A methodology to integrate geographical information system (GIS) data with large-scale pedestrian simulations has been developed. Advances in automatic data acquisition and archiving from GIS databases, automatic input for pedestrian simulations, as well as scalable pedestrian simulation tools have made it possible to simulate pedestrians at the individual level for complete cities in real time. An example that simulates the evacuation of the city of Barcelona demonstrates that this is now possible. This is the first step towards a fully integrated crowd prediction and management tool that takes into account not only data gathered in real time from cameras, cell phones or other sensors, but also merges these with advanced simulation tools to predict the future state of the crowd.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Blue VJ, Adler JL (1998) Emergent fundamental pedestrian flows from cellular automata microsimulation. Transp. Res. Rec. 1644:29–36

    Article  Google Scholar 

  2. Blue VJ, Adler JL (2002) Flow capacities from cellular automata modeling of proportional splits of pedestrians by direction. In: Schreckenberg M, Sharma SD (eds) Pedestrian and evacuation dynamics. Springer, Berlin, pp 115–22

    Google Scholar 

  3. Corrigan A, Camelli FF, Löhner R, Wallin J (2011) Running unstructured grid based CFD solvers on modern graphics hardware. Int J Numer Methods Fluids 66:221–229

    Article  MathSciNet  MATH  Google Scholar 

  4. Corrigan A, Camelli F, Löhner R, Mut F (2012) Semi-automatic porting of a large-scale fortran CFD code to GPUs. Int J Numer Methods Fluids 69(2):314–331

    Article  MATH  Google Scholar 

  5. Curtis S, Manocha D (2012) Pedestrian simulation using geometric reasoning in velocity space. Pedestr Evac Dyn

  6. Courty N, Musse S (2005) Simulation of large crowds including gaseous phenomena. In: Proceedings of IEEE computer graphics international 2005, New York pp 206–212

  7. Deere SJ, Galea ER, Lawrence PJ (2009) A systematic methodology to assess the impact of human factors in ship design. Appl Math Modell 33(2):867–883

    Article  Google Scholar 

  8. Dijkstra J, Jesurun J, Timmermans H (2002) A multi-agent cellular automata model of pedestrian movement. In: Schreckenberg M, Sharma SD (eds) Pedestrian and evacuation dynamics. Springer, Berlin, pp 173–180

    Google Scholar 

  9. Fruin JJ (1971) Pedestrian planning and design. Metropolitan association of urban designers and environmental planners, New York

    Google Scholar 

  10. Guy SJ, Chhugani J, Kim C, Satish N, Lin M, Manocha D, Dubey P (2009) ClearPath: highly parallel collision avoidance for multi-agent simulation. In: Fellner D, Spencer S (eds) Proceedings of ACM SIGGRAPH/ eurographics symposium on computer animation. Association of Computing Machinery, New York, pp 177–187

    Google Scholar 

  11. Guy SJ, Chhugani J, Curtis S, Dubey P, Lin M, Manocha D (2010) PLEdestrians: a least-effort approach to crowd simulation. Eurographics/ACM SIGGRAPH Symposium on Computer Animation, Madrid, Spain

    Google Scholar 

  12. Helbing D, Molnar P (1995) Social force model for pedestrian dynamics. Phys Rev E 51:4282–4286

    Article  Google Scholar 

  13. Helbing D, Farkas IJ, Molnár P, Vicsek T (2002) Simulation of pedestrian crowds in normal and evacuation situations. In: Schreckenberg M, Sharma SD (eds) Pedestrian and evacuation dynamics. Springer, Berlin, pp 21–58

    Google Scholar 

  14. Hughes RL (2003) The flow of human crowds. Annu Rev Fluid Mech 35:169–182

    Article  MathSciNet  MATH  Google Scholar 

  15. Isenhour M, Löhner R (2014) Verification of a pedestrian simulation tool using the NIST recommended test cases. The conference in pedestrian and evacuation dynamics 2014 (PED2014). Transp Res Proc 2:237–245

    Article  Google Scholar 

  16. Isenhour M, Löhner R (2014) Verification of a pedestrian simulation tool using the NIST stairwell evacuation data. The conference in pedestrian and evacuation dynamics 2014 (PED2014). Transp Res Proc 2:739–744

    Article  Google Scholar 

  17. Isenhour M (2016) Simulating occupant response to emergency situations. Ph.D. Thesis, George Mason University, Fairfax, VA

  18. Kapadia M, Pelechano N, Allbeck J, Badler N (2015) Virtual crowds: steps toward behavioral realism. Morgan & Claypool Publishers

  19. Karmakharm T, Richmond P, Romano DM (2010) Agent-based large scale simulation of pedestrians with adaptive realistic navigation vector fields. In: Collomosse J, Grimstead I (eds) EG UK theory and practice of computer graphics 2010

  20. Kessel A, Klüpfel H, Wahle J, Schreckenberg M (2002) Microscopic simulation of pedestrian crowd motion. In: Schreckenberg M, Sharma SD (eds) Pedestrian and evacuation dynamics. Springer, Berlin, pp 193–202

    Google Scholar 

  21. Klüpfel HL (2003) A cellular automation model for crowd movement and egress simulation. Ph.D. Dissertation: Falkutät 4, Univ. Duisburg-Essen

  22. Knuth DE (1973) The art of computer programming, vol 1-3. Addison-Wesley, Reading

  23. Lakoba TI, Kaup DJ, Finkelstein NM (2005) Modifications of the Helbing-Molnár-Farkas-Vicsek social force model for pedestrian evolution. Simulation 81:339

    Article  Google Scholar 

  24. Langston PA, Masling R, Asmar BN (2006) Crowd dynamics discrete element multi-circle model. Saf Sci 44:395–417

    Article  Google Scholar 

  25. Löhner R, Ambrosiano J (1990) A vectorized particle tracer for unstructured grids. J Comp Phys 91(1):22–31

    Article  MATH  Google Scholar 

  26. Löhner R (2007) The empty bin: a data structure for spatial search of time-varying data. Commun Numer Methods Eng 23(12):1111–1119

    Article  MathSciNet  MATH  Google Scholar 

  27. Löhner R (2008) Applied CFD techniques, 2nd edn. Wiley, Hoboken

    MATH  Google Scholar 

  28. Löhner R (2010) On the modeling of pedestrian motion. Appl Math Model 34(2):366–382

    Article  MathSciNet  MATH  Google Scholar 

  29. Löhner R, Baqui M, Haug E, Muhamad B (2016) Real-time micro-modelling of a million pedestrians. Eng Comput 33(1):217–237

    Article  Google Scholar 

  30. Lord J, Meacham B, Moore A, Fahy R, Proulx G (2005) Guide for evaluating the predictive capabilities of computer egress models. NIST CGR 06-886, National Institute of Standards and Technology, Gaithersburg, MD

  31. Munjiza A, Andrews KRF (1998) NBS contact detection algorithm for bodies of similar size. Int J Numer Methods Eng 43:131–149

    Article  MATH  Google Scholar 

  32. Munjiza A, Rougier E, John NWM (2006) MR linear contact detection algorithm. Int J Numer Methods Eng 66:46–71

    Article  MATH  Google Scholar 

  33. Pelechano N, Badler NI (2006) Modeling crowd and trained leader behavior during building evacuation. IEEE Comput Graph Appl 26(6):80–86

    Article  Google Scholar 

  34. Pelechano N, Allbeck J, Badler NI (2008) Virtual crowds: methods, simulation and control. Morgan & Claypool, San Rafael

    Google Scholar 

  35. Predtetschenski WM, Milinski AI (1971) Personenströme in Gebäuden—Berechnungsmethoden für die Projektierung. Verlaggesellschaft Rudolf Müller, Köln-Braunsfeld

  36. Quinn MJ, Metoyer RA, Hunter-Zaworski K (2003) Parallel implementation of the social forces model. In: Proceedings of 2nd international conference in pedestrian and evacuation dynamics, pp 63–74

  37. Schadschneider A (2002) Cellular automaton approach to pedestrian dynamics–theory. In: Schreckenberg M, Sharma SD (eds) Pedestrian and evacuation dynamics. Springer, Berlin, pp 75–86

    Google Scholar 

  38. Schreckenberg M, Sharma SD (eds) (2002) Pedestrian and evacuation dynamics. Springer, Berlin

    MATH  Google Scholar 

  39. Sedgewick R (1983) Algorithms. Addison-Wesley, Reading

  40. Steed A, Abou-Haidar R (2003) Partitioning crowded virtual environments. In: The proceedings of ACM symposium on virtual reality software and technology (VRST’03), New York, pp 7–14

  41. Sud A, Gayle R, Andersen E, Guy S (2007) Ming Lin and D. Manocha—Real-time navigation of independent agents using adaptive roadmaps. In: ACM symposium on virtual reality software and technology

  42. Teknomo K, Takeyama Y, Inamura H (March 2000) Review on microscopic pedestrian simulation model. In: Proceedings of Japan society of civil engineering conference. Morioka, Japan

  43. The Conference on Pedestrian and Evacuation Dynamics, a bi-annual Conference; 2014 edition was in Delft, The Netherlands; see Transportation Research Procedia (2014)

  44. The Conference on Pedestrian and Evacuation Dynamics, a bi-annual Conference; 2016 edition was in Hefei, China; see (W. Song, J. Ma and L. Fu eds.), University of Science and Technology Press, Hefei, China (2016). see Transportation Research Procedia (2014)

  45. Torrens PM (2012) Moving agent pedestrians through space and time. Ann Assoc Am Geograph 102(1):35–66

    Article  Google Scholar 

  46. Thalmann D, Musse SR (2007) Crowd simulation. Springer, London

    Book  Google Scholar 

  47. Vigueras G, Lozano M, Ordun JM, Grimaldo F (2010) A comparative study of partitioning methods for crowd simulations. Appl Soft Computing 10:225–235

    Article  Google Scholar 

  48. Vishton PM, Cutting JE (1995) Wayfinding, displacements, and mental maps: velocity fields are not typically used to determine ones aimpoint. J Exp Psychol 21(5):978–995

    Google Scholar 

  49. Zhang J, Britto D, Chraibi M, Löhner R, Haug E, Gawenat B (2014) Qualitative validation of PEDFLOW for description of unidirectional pedestrian dynamics. The conference in pedestrian and evacuation dynamics 2014 (PED2014). Transp Res Proc 2:733–738

    Article  Google Scholar 

Download references

Acknowledgements

It is a pleasure to acknowledge the many collaborators who helped to bring to fruition this effort. Bodo Rasch, whose visionary ideas started this work. Achmed Rasch, Mohammed Khaled Gdoura, Timo Leucht, Stefan Haenlein and Iris Treffinger from the visualization department at SL Rasch. Juergen Bradatsch, Bernhard Gawenat, Britto Muhammad and Prabhudev Dambalmath from the engineering department at SL Rasch. And, from the CFD Center at GMU, Fernando Camelli, Michelle Isenhour and Muhammad Baqui. Furthermore, the Institut Cartogràfic i Geològic de Catalunya (ICGC, http://www.icgc.cat/) and the Cartobcn del Ajuntament de Barcelona (http://w20.bcn.cat/cartobcn/) for the data used to generate the highly accurate and detailed model of the city of Barcelona.

Author information

Authors and Affiliations

  1. Center for Computational Fluid Dynamics, M.S. 6A2, College of Science, George Mason University, Fairfax, VA, 22030-4444, USA

    Rainald Löhner

  2. Institute for Scientific Architecture, SL Rasch, 70771, Leinfelden-Echterdingen, Germany

    Eberhard Haug

  3. CIMNE, Universidad Politécnica de Cataluña, Barcelona, Spain

    Claudio Zinggerling & Eugenio Oñate

Authors
  1. Rainald Löhner
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Eberhard Haug
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Claudio Zinggerling
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Eugenio Oñate
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rainald Löhner.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Löhner, R., Haug, E., Zinggerling, C. et al. Real-time micro-modelling of city evacuations. Comp. Part. Mech. 5, 71–86 (2018). https://doi.org/10.1007/s40571-016-0154-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40571-016-0154-z

Keywords

Search

Navigation