Subject The research investigates key processes of urban life and its maintenance, including food supply, infrastructure, fire security, quality and accessibility of medical services, etc. The article also discusses the creation of a system supporting the Smart City decision-making process. Objectives The research develops methods and tools to manage the Smart City system through system dynamics and agent-based modeling. Methods Using simulation modeling, namely system dynamics and agent-based modeling (supported via Powersim and AnyLogic), we evaluate how multiple guiding parameters influence crucial characteristics of the Smart City system. Results We devised an approach to designing the Smart City system through methods of system dynamics and agent-based modeling (supported via Powersim and AnyLogic) intended to streamline the decision making process for reasonable urban planning. Conclusions and Relevance We propose the consolidated architecture of the Smart City decision-making system integrating the simulation models, data storage and city monitoring subsystem. The article describes the cases of simulation models implemented via Powersim and AnyLogic to support rational urban planning. The simulation models will significantly improve the quality of urban environment, satisfy the demand for food products, provide access to healthcare services and ensure effective rescue actions in case of emergency.
Keywords: simulation modeling, smart city, agent-based modeling, system dynamics
Akopov A.S., Beklaryan G.L. Modelling the Dynamics of the “Smarter Region”. In: Proceedings of 2014 IEEE Conference on Computational Intelligence for Financial Engineering & Economics. IEEE, 2014, pp. 203–209. URL: Link
Komninos N. Intelligent Cities and Globalisation of Innovation Networks. Routledge, 2008, p. 308.
Akopov A.S. Designing of Integrated System-Dynamics Models for an Oil Company. International Journal of Computer Applications in Technology, 2012, vol. 45, no. 4, pp. 220–230. URL: Link
Meadows D.H. Limits to Growth: A Report for the Club of Rome's Project on the Predicament of Mankind. New York, Universe Books, 1972.
Akopov A.S., Beklaryan L.A. [An Agent Model of Crowd Behavior in Emergencies]. Avtomatika i telemekhanika = Automation and Remote Control, 2015, no. 10, pp. 1817–1827. (In Russ.)
Bakhtizin A.R. Agent-orientirovannye modeli ekonomiki [Agent-based models of economics]. Moscow, Ekonomika Publ., 2008, 279 p.
Makarov V.L., Bakhtizin A.R. Sotsial'noe modelirovanie – novyi komp'yuternyi proryv (agent-orientirovannye modeli) [Social Modeling – New Computer Breakthrough (Agent-based models)]. Moscow, Ekonomika Publ., 2013, 295 p.
Forrester J. Industrial Dynamics – A Major Breakthrough for Decision Makers. Harvard Business Review, 1958, vol. 36, no. 4, pp. 37–66.
Forrester J. Urban Dynamics. Pegasus Communications. Waltham, MT, USA, 1969.
Akopov A.S., Beklaryan L.A., Saghatelyan A.K. Agent-Based Modelling for Ecological Economics: A case study of the Republic of Armenia. Ecological Modelling, 2017, vol. 346, pp. 99–118.
Beklaryan L.A., Akopov A.S., Beklaryan A.L., Saghatelyan A.K. Agent-Based Simulation Modelling for Regional Ecological-Economic Systems. A case study of the Republic of Armenia. Journal of Machine Learning and Data Analysis, 2016, vol. 2, no. 1, pp. 104–115.
Beklaryan A.L., Akopov A.S. [Simulation of human crowd behavior based on intellectual dynamics of interacting agents]. Biznes-informatika= Business Informatics, 2015, no. 1, pp. 69–77. URL: Link (In Russ.)
Akopov A.S., Beklaryan A.L. Simulation of Human Crowd Behavior in Extreme Situations. International Journal of Pure and Applied Mathematics, 2012, vol. 79, no. 1, pp. 121–138.
Beklaryan A.L., Akopov A.S. Simulation of Agent-Rescuer Behaviour in Emergency Based on Modified Fuzzy Clustering. Proceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems (AAMAS'16). Richland, International Foundation for Autonomous Agents and Multiagent Systems, 2016, pp. 1275–1276.
Akopov A.S., Heventsev M.A. A Multi-Agent Genetic Algorithm for Multi-Objective Optimization. Proceedings of IEEE International Conference on Systems, Man and Cybernetics. Manchester, 13–16 October 2013, IEEE, pp. 1391–1395.
Johansson A., Helbing D., Al-Abideen H.Z., Al-Bosta S. From Crowd Dynamics to Crowd Safety: A Video-Based Analysis. Advances in Complex Systems, 2008, no. 4, pp. 497–527.
Zagoruiko N.G., Elkina V.N., Lbov G.S. Algoritmy obnaruzheniya empiricheskikh zakonomernostei [Algorithms for detecting empirical patterns]. Novosibirsk, Nauka Publ., 1985, 999 p.