Skip to main content

Social Distancing and Behavior Modeling with Agent-Based Simulation

  • 499 Accesses

Part of the Communications in Computer and Information Science book series (CCIS,volume 1465)

Abstract

The research discusses applying agent-based simulation (ABS) technology to analyze the social distancing in public space during the COVID-19 pandemic to facilitate design and planning decisions. The ABS is used to simulate pedestrian flow and construct the micro-level complexity within a simulated environment. This paper describes the various computational methods related to the ABS and design space under the new social distancing guidelines. We focus on the linear phases of agent activities, including (1) environmental query, (2) waiting in a zone, (3) waiting in a queue, and (4) tasks (E-Z-Q-T) in response to design iterations related to crowd control and safety distance. The design project is extended to the agents’ interactions driven by a set of tasks in a simulated grocery store, restaurant, and public restroom. We applied a quantitative analysis method and proximity analysis to evaluate architectural layouts and crowd control strategies. We discussed social distancing, pedestrian flow efficiency, public accessibility, and ways of reducing congestion through the intervention of the E-Z-Q-T phases.

Keywords

  • Agent-based simulation
  • Social distancing
  • Crowd control

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-981-19-1280-1_8
  • Chapter length: 10 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   84.99
Price excludes VAT (USA)
  • ISBN: 978-981-19-1280-1
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   109.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.
Fig. 8.

Notes

  1. 1.

    “The simulated behaviors of cellular automation are often unpredictable and lack purposive planning goals….and the interactions among agents, complex social behavior cannot be simulated through space syntax.“ (Tang, 2018).

  2. 2.

    These semipublic, semiprivate places such as restaurants, bars, gyms, houses of worship, barbershops, coffee shops, post offices, main streets, beer gardens, bookstores, parks, community centers, and gift shops—inexpensive places where people come together, and life happens.

  3. 3.

    Oasys MassMotion is an advanced crowd simulation software that uses crowd modelling technology to provide leading technology to designers, operators and owners with clear information about crowding, usage patterns and occupant safety in a facility. https://www.oasys-software.com/products/pedestrian-simulation/massmotion/.

References

  1. Batty, M.: Cities and Complexity, Understanding Cities with Cellular Automata, Agent-Based Models, and Fractals. MIT Press, Cambridge (2007)

    Google Scholar 

  2. Tang, M., Auffrey, C.: Advanced digital tools for updating overcrowded rail stations: using eye tracking, virtual reality, and crowd simulation to support design decision-making. Urban Rail Transit 4, 249–256 (2018)

    CrossRef  Google Scholar 

  3. Schaumann, D., et al.: Toward a multi-level and multi-paradigm platform for building occupant simulation. In: Symposium on Simulation for Architecture and Urban Design (2019)

    Google Scholar 

  4. Shirvani, M., Georges K., Paul R.: Agent-based simulator of dynamic flood‐people interactions. J. Flood Risk Manag. 14(2) (2021). https://doi.org/10.1111/jfr3.12695

  5. Zarrinmehr, S., Asl, M.R., Yan, W., Clayton, M.: Optimizing building layout to minimize the level of danger in panic evacuation using genetic algorithm and agent-based crowd simulation. In: BIMSIM Group (2021). Accessed 15 Mar 2021

    Google Scholar 

  6. David, G., Rorigo, L.: Context-aware multi-agent systems. In: ACADIA Conference Proceeding (2014)

    Google Scholar 

  7. Peter, B.: Emergence as a design strategy in urban development using agent-oriented modeling in simulation of reconfiguration of the urban structure. In: ECAADE 2012 Conference Proceeding (2012)

    Google Scholar 

  8. Hao, H., Ting, L.: Floating bubbles, an agent-based system for layout planning. In: CAADRIA 2010 Conference Proceeding (2010)

    Google Scholar 

  9. Gideon, A.: Agent-based social pedestrian simulation for the validation of urban planning recommendations. In: SIGRADI 2012 Conference Proceeding (2012)

    Google Scholar 

  10. Kiefer, A.W., Bonneaud, S., Rio, K., Warren, W.H.: Quantifying the coherence of pedestrian groups. In: Proceedings of the Cognitive Science Society, Berlin, Germany (2013)

    Google Scholar 

  11. Oasys webpage. https://www.oasys-software.com/news/can-pedestrian-simulation-tools-really-model-humans-accurately/, Accessed 5 July 2021

  12. Reynolds, C.W.: Flocks, herds, and schools: a distributed behavioral model. In: Computer Graphics, SIGGRAPH 1987 Conference Proceedings, pp. 25–34 (1987)

    Google Scholar 

  13. Unreal engine webpage: Environment Query System: Overview. Accessed 5 July 2021, https://docs.unrealengine.com/en-US/Engine/ArtificialIntelligence/EQS/EQSOverview/index.html

Download references

Acknowledgments

Thanks to the students Sarah Auger, Maddison DeWitt, Brittany Ellis, Andy Failor, Lisa Garcia, Ashley Kasel, An Le, Hannah Loftspring, Kyle Munn, Deborah Park, Sabrina Ramsay, Haley Schulte, Brayden Templeton, Pwint Wati Oo (Audrey) participated the project in Fall 2021 at the University of Cincinnati. Thanks to UC Forward, Price Hill Will, and Meiser’s Fresh Grocery & Deli provided advice and support. Thanks to Nathan Deininger provided proofread and editing.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ming Tang .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2022 Springer Nature Singapore Pte Ltd.

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Tang, M. (2022). Social Distancing and Behavior Modeling with Agent-Based Simulation. In: Gerber, D., Pantazis, E., Bogosian, B., Nahmad, A., Miltiadis, C. (eds) Computer-Aided Architectural Design. Design Imperatives: The Future is Now. CAAD Futures 2021. Communications in Computer and Information Science, vol 1465. Springer, Singapore. https://doi.org/10.1007/978-981-19-1280-1_8

Download citation

  • DOI: https://doi.org/10.1007/978-981-19-1280-1_8

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-1279-5

  • Online ISBN: 978-981-19-1280-1

  • eBook Packages: Computer ScienceComputer Science (R0)