Population behavior, social networks, transportations, infrastructures, industrial and urban simulation

Abstract

Crisis prevention and management are critical tasks for a public administration. In facts, a complex system such s a city could be difficult to keep under control, even in regular conditions, while respect emergencies it is very hard to finalize good preventive and operational plans. In order to succeed in such tasks, it is necessary to have proper support tools, capable to evaluate alternatives and predict effect of decisions in different conditions. To address such issues, the authors developed PONTUS (Population Behavior, social Networks, Transportations, Infrastructures and Industrial & Urban Simulation) which combines ability to model natural disasters with advanced human behaviors of the population.

City Simulation | Decision Support Tool

References

1. Bruzzone A.G., Bocca E., Longo F., Massei M., 2007. Training and recruitment in logistics node design by using web-based simulation. International Journal of Internet Manufacturing and Services, vol. 1, no. 1, pp. 32-50.
2. Bruzzone, A.G., Agresta, M., Sinelshchikov, K. (2017a). Simulation as decision support system for
   disaster prevention. SESDE 2017, Barcelona, Sept.
3. Bruzzone, A.G., Massei, M., Agresta, M., Sinelshchikov, K., Di Matteo, R. (2017b). Agile solutions & data analitics for logistics providers based on simulation. HMS 2017, Barcelona, Spain.
4. Bruzzone, A.G., Massei, M., Maglione, G.L., Sinelshchikov, K., Di Matteo, R. (2017c). A strategic serious game addressing system of systems engineering. MAS 2017, Barcelona, Spain.
5. Bruzzone A.G., Massei M., Agresta M., Poggi S., Camponeschi F. & M. (2014) "Addressing strategic
   challenges on mega cities through MS2G", Proc. of MAS2014, Bordeaux, September
6. Bruzzone, A. G. (2013). Intelligent agent-based simulation for supporting operational planning in
   country reconstruction. Int.Journal of Simulation and Process Modelling, 8(2-3), 145-159.
7. Chanson, H. (2004). Hydraulics of open channel flow. Elsevier
8. Chen, J., Hill, A. A., & Urbano, L. D. (2009). A GISbased model for urban flood inundation. Journal of
   Hydrology, 373(1-2), 184-192
9. Duley, F. L., & Kelly, L. L. (1939). Effect of soil type, slope, and surface conditions on intake of water
10. Hsu W., Tseng C., Chiang W., Chen C., 2012. Risk and uncertainty analysis in the planning stages of a risk
    decision-making process. Natural Hazards, vol. 61, no. 3, pp. 1355-1365
11. Jasper, K., Gurtz, J., & Lang, H. (2002). Advanced flood forecasting in Alpine watersheds by coupling
    meteorological observations and forecasts with a distributed hydrological model. Journal of hydrology, 267(1-2), 40-52.
12. Kia, M. B., Pirasteh, S., Pradhan, B., Mahmud, A. R., Sulaiman, W. N. A., & Moradi, A. (2012). An artificial neural network model for flood simulation using GIS: Johor River Basin, Malaysia. Environmental Earth Sciences, 67(1), 251-264.
13. Lanza, S. G. (2003). Flood hazard threat on cultural heritage in the town of Genoa (Italy). Journal of
    Cultural Heritage, 4(3), 159-167
14. Liu, X., Qiu, X., Chen, B., & Huang, K. (2012, July). Cloud-based simulation: The state-of-the-art computer simulation paradigm. In Principles of Advanced and Distributed Simulation (PADS), 2012 ACM/IEEE/SCS 26th Workshop on (pp. 71-74). IEEE.
15. Longo F., 2012. Supply chain security: An integrated framework for container terminal facilities.
    International Journal of Simulation and Process Modelling, vol. 7, no. 3, pp. 159-167
16. Perrow C., 2011. The next catastrophe: Reducing our vulnerabilities to natural, industrial, and terrorist disasters. In The Next Catastrophe: Reducing Our Vulnerabilities to Natural, Industrial, and Terrorist Disasters, pp. 1-377.
17. Van Steen, M. & Tanenbaum, A. S. (2017). Distributed systems: principles and paradigms. Prentice-Hall