Skip to main content
SpringerLink
Log in

Simulation-Aided Crowd Management: A Multi-scale Model for an Urban Case Study

  • Conference paper
  • First Online:
Agent Based Modelling of Urban Systems (ABMUS 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10051))

Included in the following conference series:

Abstract

Safety, security, and comfort of pedestrian crowds during large gatherings are heavily influenced by the layout of the underlying environment. This work presents a systematic agent-based simulation approach to appraise and optimize the layout of a pedestrian environment in order to maximize safety, security, and comfort. The performance of the approach is demonstrated based on annual “Salone del mobile” (Design Week) exhibition in Milan, Italy. Given the large size of the scenario and the proportionally high number of simultaneously present pedestrians, the computational costs of a pure microscopic simulation approach would make this hardly applicable, whereas a multi-scale approach, combining simulation models of different granularity, provides a reasonable trade off between a detailed management of individual pedestrians and possibility to effectively carry out what-if analyses with different environmental configurations. The paper will introduce the scenario, the base model and the alternatives, discussing the achieved results.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    http://salonemilano.it/en-us/VISITORS/Salone-Internazionale-del-Mobile/Exhibition-fact-sheet.

  2. 2.

    http://www.tortonadesignweek.com/.

  3. 3.

    http://www.matsim.org.

References

  1. Anh, N.T.N., Daniel, Z.J., Du, N.H., Drogoul, A., An, V.D.: A hybrid macro-micro pedestrians evacuation model to speed up simulation in road networks. In: Dechesne, F., Hattori, H., Mors, A., Such, J.M., Weyns, D., Dignum, F. (eds.) AAMAS 2011. LNCS (LNAI), vol. 7068, pp. 371–383. Springer, Heidelberg (2012). doi:10.1007/978-3-642-27216-5_28

    Chapter  Google Scholar 

  2. Bandini, S., Crociani, L., Vizzari, G.: An approach for managing heterogeneous speed profiles in cellular automata pedestrian models. J. Cell. Automata (in press)

    Google Scholar 

  3. Blue, V., Adler, J.: Emergent fundamental pedestrian flows from cellular automata microsimulation. Transp. Res. Rec. J. Transp. Res. Board 1644, 29–36 (1998)

    Article  Google Scholar 

  4. Bourr, E., Lesort, J.B.: Mixing microscopic representations of traffic flow: hybrid model based on Lighthill-Whitham-Richards theory. Transp. Res. Rec. 1852, 193–200 (2003)

    Article  Google Scholar 

  5. Burghardt, S., Seyfried, A., Klingsch, W.: Performance of stairs-fundamental diagram and topographical measurements. Transp. Res. Part C Emerg. Technol. 37, 268–278 (2013)

    Article  Google Scholar 

  6. Burghout, W., Koutsopoulos, H., Andréasson, I.: Hybrid mesoscopic-microscopic traffic simulation. Transp. Res. Rec. 1934, 218–225 (2005)

    Article  Google Scholar 

  7. Burghout, W., Wahlstedt, J.: Hybrid traffic simulation with adaptive signal control. Transp. Res. Rec. 1999, 191–197 (2007)

    Article  Google Scholar 

  8. Burstedde, C., Klauck, K., Schadschneider, A., Zittartz, J.: Simulation of pedestrian dynamics using a two-dimensional cellular automaton. Phys. A Stat. Mech. Appl. 295(3–4), 507–525 (2001)

    Article  MATH  Google Scholar 

  9. Cascetta, E.: A stochastic process approach to the analysis of temporal dynamics in transportation networks. Transp. Res. B 23B(1), 1–17 (1989)

    Article  Google Scholar 

  10. Chooramun, N., Lawrence, P., Galea, E.: Implementing a hybrid space discretisation within an agent based evacuation model. In: Peacock, R., Kuligowski, E., Averill, J. (eds.) Pedestrian and Evacuation Dynamics 2010, pp. 449–458. Springer, Heidelberg (2011). doi:10.1007/978-1-4419-9725-8_40

    Chapter  Google Scholar 

  11. Chraibi, M., Seyfried, A., Schadschneider, A.: Generalized centrifugal-force model for pedestrian dynamics. Phys. Rev. E 82(4), 46111 (2010)

    Article  Google Scholar 

  12. Crociani, L., Lämmel, G.: Multidestination pedestrian flows in equilibrium: a cellular automaton-based approach. Comput. Aided Civ. Infrastruct. Eng. 31(2016), 432–448 (2016)

    Article  Google Scholar 

  13. Crociani, L., Lämmel, G., Vizzari, G.: Multi-scale simulation for crowd management: a case study in an urban scenario. In: Osman, N., Sierra, C. (eds.) AAMAS 2016. LNCS (LNAI), vol. 10002, pp. 147–162. Springer, Heidelberg (2016). doi:10.1007/978-3-319-46882-2_9

    Chapter  Google Scholar 

  14. Crociani, L., Manenti, L., Vizzari, G.: MAKKSim: MAS-based crowd simulations for designer’s decision support. In: Demazeau, Y., Ishida, T., Corchado, J.M., Bajo, J. (eds.) PAAMS 2013. LNCS (LNAI), vol. 7879, pp. 25–36. Springer, Heidelberg (2013). doi:10.1007/978-3-642-38073-0_3

    Chapter  Google Scholar 

  15. Dada, J.O., Mendes, P.: Multi-scale modelling and simulation in systems biology. Integr. Biol. 3(2), 86–96 (2011)

    Article  Google Scholar 

  16. Dijkstra, E.: A note on two problems in connexion with graphs. Numer. Math. 1, 269–271 (1959)

    Article  MathSciNet  MATH  Google Scholar 

  17. Espié, S., Gattuso, D., Galante, F.: A hybrid traffic model coupling macro and behavioural micro simulation. Annual Meeting Preprint 06-2013, Transportation Research Board, Washington D.C. (2006)

    Google Scholar 

  18. Flötteröd, G., Lämmel, G.: Bidirectional pedestrian fundamental diagram. Transp. Res. Part B Methodol. 71(C), 194–212 (2015)

    Google Scholar 

  19. Gawron, C.: An iterative algorithm to determine the dynamic user equilibrium in a traffic simulation model. Int. J. Mod. Phys. C 9(3), 393–407 (1998)

    Article  Google Scholar 

  20. Helbing, D.: A fluid dynamic model for the movement of pedestrians. arXiv preprint cond-mat/9805213 (1998)

    Google Scholar 

  21. Helbing, D., Hennecke, A., Shvetsov, V., Treiber, M.: Micro- and macro-simulation of freeway traffic. Math. Comput. Model. 35, 517–547 (2002)

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  Google Scholar 

  23. Henderson, L.: The statistics of crowd fluids. Nature 229(5284), 381–383 (1971)

    Article  Google Scholar 

  24. Hoogendoorn, S., Bovy, P.: Dynamic user-optimal assignment in continuous time and space. Transp. Res. Part B Methodol. 38(7), 571–592 (2004)

    Article  Google Scholar 

  25. Krajzewicz, D., Erdmann, J., Behrisch, M., Bieker, L.: Recent development and applications of SUMO - Simulation of Urban MObility. Int. J. Adv. Syst. Meas. 5(3&4), 128–138 (2012)

    Google Scholar 

  26. Kretz, T., Lehmann, K., Hofsäß, I.: User equilibrium route assignment for microscopic pedestrian simulation. Adv. Complex Syst. 17(2), 1450010 (2014)

    Google Scholar 

  27. Lämmel, G., Chraibi, M., Kemloh Wagoum, A., Steffen, B.: Hybrid multi- and inter-modal transport simulation: a case study on large-scale evacuation planning. Transp. Res. Rec. (to appear)

    Google Scholar 

  28. Lämmel, G., Flötteröd, G.: Towards system optimum: finding optimal routing strategies in time-dependent networks for large-scale evacuation problems. In: Mertsching, B., Hund, M., Aziz, Z. (eds.) KI 2009. LNCS (LNAI), vol. 5803, pp. 532–539. Springer, Heidelberg (2009). doi:10.1007/978-3-642-04617-9_67

    Chapter  Google Scholar 

  29. Lämmel, G., Flötteröd, G.: A CA model for bidirectional pedestrian streams. Procedia Comput. Sci. 52, 950–955 (2015)

    Article  Google Scholar 

  30. Lämmel, G., Grether, D., Nagel, K.: The representation and implementation of time-dependent inundation in large-scale microscopic evacuation simulations. Transp. Res. Part C Emerg. Technol. 18(1), 84–98 (2010)

    Article  Google Scholar 

  31. Lämmel, G., Klüpfel, H., Nagel, K.: The MATSim network flow model for traffic simulation adapted to large-scale emergency egress and an application to the evacuation of the Indonesian city of Padang in case of a tsunami warning. In: Timmermans, H. (ed.) Pedestrian Behavior, pp. 245–265. Emerald Group Publishing Limited (2009). Chap. 11

    Google Scholar 

  32. Lämmel, G., Seyfried, A., Steffen, B.: Large-scale and microscopic: a fast simulation approach for urban areas. Annual Meeting Preprint 14-3890, Transportation Research Board, Washington, D.C. (2014)

    Google Scholar 

  33. Liao, W., Seyfried, A., Zhang, J., Boltes, M., Zheng, X., Zhao, Y.: Experimental study on pedestrian flow through wide bottleneck. Transp. Res. Procedia 2, 26–33 (2014)

    Article  Google Scholar 

  34. Michon, J.: A critical view of driver behavior models: what do we know, what should we do? In: Evans, L., Schwing, R.C. (eds.) Hum. Behav. Traffic Saf., pp. 485–524. Springer, New York (1985)

    Chapter  Google Scholar 

  35. Nash, J.: Non-cooperative games. Ann. Math. 54(2), 286–295 (1951)

    Article  MathSciNet  MATH  Google Scholar 

  36. Raney, B., Nagel, K.: Iterative route planning for large-scale modular transportation simulations. Future Gener. Comput. Syst. 20(7), 1101–1118 (2004)

    Article  Google Scholar 

  37. Rupprecht, T., Klingsch, W., Seyfried, A.: Influence of geometry parameters on pedestrian flow through bottleneck. In: Pedestrian and Evacuation Dynamics 2010, pp. 71–80 (2011)

    Google Scholar 

  38. Simon, P., Esser, J., Nagel, K.: Simple queueing model applied to the city of Portland. Int. J. Mod. Phys. 10(5), 941–960 (1999)

    Article  Google Scholar 

  39. von Sivers, I., Köster, G.: Dynamic stride length adaptation according to utility and personal space. Transp. Res. Part B Methodol. 74(30), 104–117 (2014)

    Google Scholar 

  40. Taillandier, P., Vo, D.-A., Amouroux, E., Drogoul, A.: GAMA: a simulation platform that integrates geographical information data, agent-based modeling and multi-scale control. In: Desai, N., Liu, A., Winikoff, M. (eds.) PRIMA 2010. LNCS (LNAI), vol. 7057, pp. 242–258. Springer, Heidelberg (2012). doi:10.1007/978-3-642-25920-3_17

    Chapter  Google Scholar 

  41. Weidmann, U.: Transporttechnik der Fussgänger - Transporttechnische Eigenschaftendes Fussgängerverkehrs (Literaturstudie). Literature Research 90, Institut füer Verkehrsplanung, Transporttechnik, Strassen- und Eisenbahnbau IVT an der ETH Zürich (1993)

    Google Scholar 

  42. Zhang, J., Klingsch, W., Schadschneider, A., Seyfried, A.: Transitions in pedestrian fundamental diagrams of straight corridors and t-junctions. J. Stat. Mech. Theor. Exp. 2011(06), P06004 (2011)

    Google Scholar 

  43. Zhang, J., Klingsch, W., Schadschneider, A., Seyfried, A.: Ordering in bidirectional pedestrian flows and its influence on the fundamental diagram. J. Stat. Mech. Theor. Exp. 2012(02), 9 (2012)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Complex Systems and Artificial Intelligence Research Centre, University Milano-Bicocca, Milan, Italy

    Luca Crociani & Giuseppe Vizzari

  2. Institute of Transportation Systems, German Aerospace Center (DLR), Berlin, Germany

    Gregor Lämmel

Authors
  1. Luca Crociani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gregor Lämmel
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Giuseppe Vizzari
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gregor Lämmel .

Editor information

Editors and Affiliations

  1. University of Wollongong , Wollongong, New South Wales, Australia

    Mohammad-Reza Namazi-Rad

  2. RMIT University , Melbourne, Victoria, Australia

    Lin Padgham

  3. University of Wollongong , Wollongong, New South Wales, Australia

    Pascal Perez

  4. Technische Universität Berlin , Berlin, Germany

    Kai Nagel

  5. Universidade Federal do Rio Grande do Sul , Rio Grande do Sul, Brazil

    Ana Bazzan

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Crociani, L., Lämmel, G., Vizzari, G. (2017). Simulation-Aided Crowd Management: A Multi-scale Model for an Urban Case Study. In: Namazi-Rad, MR., Padgham, L., Perez, P., Nagel, K., Bazzan, A. (eds) Agent Based Modelling of Urban Systems. ABMUS 2016. Lecture Notes in Computer Science(), vol 10051. Springer, Cham. https://doi.org/10.1007/978-3-319-51957-9_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-51957-9_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-51956-2

  • Online ISBN: 978-3-319-51957-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation