Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
119 tokens/sec
GPT-4o
56 tokens/sec
Gemini 2.5 Pro Pro
43 tokens/sec
o3 Pro
6 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Multilevel Modeling as a Methodology for the Simulation of Human Mobility (2403.16745v1)

Published 25 Mar 2024 in cs.PF and physics.soc-ph

Abstract: Multilevel modeling is increasingly relevant in the context of modelling and simulation since it leads to several potential benefits, such as software reuse and integration, the split of semantically separated levels into sub-models, the possibility to employ different levels of detail, and the potential for parallel execution. The coupling that inevitably exists between the sub-models, however, implies the need for maintaining consistency between the various components, more so when different simulation paradigms are employed (e.g., sequential vs parallel, discrete vs continuous). In this paper we argue that multilevel modelling is well suited for the simulation of human mobility, since it naturally leads to the decomposition of the model into two layers, the "micro" and "macro" layer, where individual entities (micro) and long-range interactions (macro) are described. In this paper we investigate the challenges of multilevel modeling, and describe some preliminary results using prototype implementations of multilayer simulators in the context of epidemic diffusion and vehicle pollution.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (23)
  1. G. D’Angelo, S. Ferretti, and V. Ghini, “Multi-level simulation of internet of things on smart territories,” Simulation Modelling Practice and Theory (SIMPAT), vol. 73, 2017.
  2. R. Sargent, J. Mize, D. Withers, and B. Zeigler, “Hierarchical modeling for discrete event simulation (panel),” in Proceedings of 1993 Winter Simulation Conference - (WSC ’93), 1993, pp. 569–572.
  3. G. D’Angelo and M. Marzolla, “New trends in parallel and distributed simulation: From many-cores to cloud computing,” Simul. Model. Pract. Theory, vol. 49, pp. 320–335, 2014.
  4. P. M. Tarwater and C. F. Martin, “Effects of population density on the spread of disease,” Complexity, vol. 6, no. 6, pp. 29–36, 2001.
  5. R. A. Sahner and K. S. Trivedi, “Reliability modeling using sharpe,” IEEE Transactions on Reliability, vol. 36, no. 2, pp. 186–193, 1987.
  6. M. Martcheva, N. Tuncer, and C. St Mary, “Coupling within-host and between-host infectious diseases models,” Biomath, vol. 4, no. 2, p. 1510091, 2015.
  7. L. Wang, Y. Zhang, Z. Wang, and X. Li, “The impact of human location-specific contact pattern on the sir epidemic transmission between populations,” International Journal of Bifurcation and Chaos, 2013.
  8. S. Chang, E. Pierson, P. W. Koh, J. Gerardin, B. Redbird, D. Grusky, and J. Leskovec, “Mobility network models of covid-19 explain inequities and inform reopening,” Nature, vol. 589, no. 7840, pp. 82–87, 2021.
  9. K. Peng, Z. Lu, V. Lin, M. R. Lindstrom, C. Parkinson, C. Wang, A. L. Bertozzi, and M. A. Porter, “A multilayer network model of the coevolution of the spread of a disease and competing opinions,” Mathematical Models and Methods in Applied Sciences, 2021.
  10. E. Cristiani, B. Piccoli, and A. Tosin, “Multiscale modeling of granular flows with application to crowd dynamics,” Multiscale Modeling & Simulation, vol. 9, no. 1, pp. 155–182, 2011.
  11. D. Ni, “Multiscale modeling of traffic flow,” Mathematica Aeterna, vol. 1, no. 1, pp. 27–54, 2011.
  12. R. Ekyalimpa, M. Werner, S. Hague, S. AbouRizk, and N. Porter, “A combined discrete-continuous simulation model for analyzing train-pedestrian interactions,” in 2016 Winter Simulation Conference (WSC).   IEEE, 2016, pp. 1583–1594.
  13. G. D’Angelo, “The simulation model partitioning problem: an adaptive solution based on self-clustering,” Simulation Modelling Practice and Theory (SIMPAT), vol. 70, pp. 1 – 20, 2017.
  14. “IEEE Standard for Modeling and Simulation (M&S) High Level Architecture (HLA)–Framework and Rules,” IEEE Std 1516-2010 (Rev. of IEEE Std 1516-2000), pp. 1–38, 2010.
  15. H. Barbosa, M. Barthelemy, G. Ghoshal, C. R. James, M. Lenormand, T. Louail, R. Menezes, J. J. Ramasco, F. Simini, and M. Tomasini, “Human mobility: Models and applications,” Physics Reports, vol. 734, pp. 1–74, 2018, human mobility: Models and applications.
  16. G. D’Angelo and S. Ferretti, “Adaptive parallel and distributed simulation of complex networks,” Journal of Parallel and Distributed Computing, vol. 163, pp. 30–44, 2022.
  17. S. Tisue and U. Wilensky, “Netlogo: A simple environment for modeling complexity,” in International conference on complex systems, vol. 21.   Boston, MA, 2004, pp. 16–21.
  18. G. D’Angelo, S. Ferretti, and L. Serena, “Parallel And Distributed Simulation (PADS) Research Group,” http://pads.cs.unibo.it, 2022.
  19. A. Hjorth, B. Head, C. Brady, and U. Wilensky, “Levelspace: A netlogo extension for multi-level agent-based modeling,” Journal of Artificial Societies and Social Simulation, vol. 23, no. 1, 2020.
  20. M. Jaxa-Rozen and J. H. Kwakkel, “Pynetlogo: Linking netlogo with python,” Jasss, vol. 21, no. 2, 2018.
  21. F. Brauer, “Compartmental models in epidemiology,” in Mathematical epidemiology.   Springer, 2008, pp. 19–79.
  22. T. E. Oliphant, “Scipy tutorial,” 2004.
  23. M. Y. Li and J. S. Muldowney, “Global stability for the seir model in epidemiology,” Mathematical biosciences, vol. 125, no. 2, pp. 155–164, 1995.
User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (4)
  1. Luca Serena (10 papers)
  2. Moreno Marzolla (29 papers)
  3. Gabriele D'Angelo (47 papers)
  4. Stefano Ferretti (58 papers)
Citations (2)

Summary

We haven't generated a summary for this paper yet.