Abstract
As the world becomes more dense, connected, and complex, it is increasingly difficult to answer “what-if” questions about our cities and populations. Most modeling and simulation tools struggle with scale and connectivity. We present a new method for creating digital twin simulations of city infrastructure and populations from open source and commercial data. We transform cellular location data into activity patterns for synthetic agents and use geospatial data to create the infrastructure and world in which these agents interact. We then leverage technologies and techniques intended for massive online gaming to create 1:1 scale simulations to answer these “what-if” questions about the future.
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References
Adiga, A., Marathe, M., Mortveit, H., Wu, S., Swarup, S.: Modeling urban transportation in the aftermath of a nuclear disaster: the role of human behavioral responses. In: The Conference on Agent-Based Modeling in Transportation Planning and Operations. Citeseer (2013)
Anderson, P.W., et al.: More is different. Science 177(4047), 393–396 (1972)
Attiya, H., Welch, J.: Distributed Computing: Fundamentals, Simulations, and Advanced Topics, vol. 19. Wiley, Hoboken (2004)
Barrett, C., et al.: Cascading failures in multiple infrastructures: from transportation to communication network. In: 2010 5th International Conference on Critical Infrastructure (CRIS), pp. 1–8. IEEE (2010)
Barrett, C.L., Eubank, S., Marathe, A., Marathe, M.V., Pan, Z., Swarup, S.: Information integration to support model-based policy informatics. Innov. J.: Public Sector Innov. J. 16(1) (2011). https://www.innovation.cc/volumes-issues/vol16-no1.htm
Goldberg, A.V., Kaplan, H., Werneck, R.F.: Reach for a*: efficient point-to-point shortest path algorithms. In: 2006 Proceedings of the Eighth Workshop on Algorithm Engineering and Experiments (ALENEX), pp. 129–143. SIAM (2006)
Kesting, A., Treiber, M., Helbing, D.: Enhanced intelligent driver model to access the impact of driving strategies on traffic capacity. Philos. Trans. R. Soc. Lond. A: Math. Phys. Eng. Sci. 368(1928), 4585–4605 (2010)
Lin, Z., Yin, M., Feygin, S., Sheehan, M., Paiement, J.F., Pozdnoukhov, A.: Deep generative models of urban mobility. IEEE Trans. Intell. Transp. Syst. (2017)
OpenCell ID. Data. http://opencellid.org/downloads. http://opencellid.org
OpenStreetMap contributors: Planet dump (2017). https://planet.osm.org. https://www.openstreetmap.org
Ren, Y., Ercsey-Ravasz, M., Wang, P., González, M.C., Toroczkai, Z.: Predicting commuter flows in spatial networks using a radiation model based on temporal ranges. Nat. Commun. 5, 5347 (2014)
Simoudis, E., Han, J., Fayyad, U.M. (eds.): A density-based algorithm for discovering clusters in large spatial databases with noise. Proceedings of the Second International Conference on Knowledge Discovery and Data Mining. AAAI Press (1996)
Southeast Michigan Council of Governments: Data portal. http://maps-semcog.opendata.arcgis.com/datasets
US Census Bureau: Tiger/line shapefiles. https://www.census.gov/cgi-bin/geo/shapefiles/index.php
Yin, M., Sheehan, M., Feygin, S., Paiement, J.F., Pozdnoukhov, A.: A generative model of urban activities from cellular data. IEEE Trans. Intell. Transp. Syst. 19(6), 1682–1696 (2017). https://doi.org/10.1109/TITS.2017.2695438
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Baeder, D. et al. (2019). Massive-Scale Models of Urban Infrastructure and Populations. In: Thomson, R., Bisgin, H., Dancy, C., Hyder, A. (eds) Social, Cultural, and Behavioral Modeling. SBP-BRiMS 2019. Lecture Notes in Computer Science(), vol 11549. Springer, Cham. https://doi.org/10.1007/978-3-030-21741-9_12
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DOI: https://doi.org/10.1007/978-3-030-21741-9_12
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