GEOCITY—A NEW DYNAMIC-SPATIAL MODEL OF URBAN ECOSYSTEM
DOI:
https://doi.org/10.2298/IJGI2302187VKeywords:
simulation model, resident, geo-object, GeoCity, urban ecosystemAbstract
In this paper the initialization of the city is considered, which consists of several steps, including the creation of city objects with their locations, creation of residents with their attributes and own daily schedules, etc. A description of the model is provided as a tuple of attributes. The adequacy of the simulation model is checked based on the statistical data from the city of Lviv, Ukraine. Generated locations of city ecosystem objects are presented. The daily schedule of residents is simulated. A possible work schedule for each specialty is given, and separate schedules are created for working days and holidays. A unique schedule is predicted for the resident, which depends on their age and work specialty. The dynamics of visits to facilities by residents on weekdays and at weekends are analyzed. Based on the conducted experiments, the adequacy of the model and its realistic reflection of the functioning of the city's ecosystem during the day are proven. It means that by using this model, researchers can assess the impact of different behavioral scenarios on the residents within the city ecosystem more reliably. This enables a better understanding of how certain actions or changes in behavior can affect the spread and control of diseases in a specific geographic area. This model has the potential to serve as a foundation for future modeling of systems at the medium and macro scales.
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References
Anderson, J. (2001). Providing a Broad Spectrum of Agents in Spatially Explicit Simulation Models: The Gensim Approach. In R. H. Gimblett (Ed.), Integrating Geographic Information Systems and Agent-Based Modeling Techniques for Understanding Social and Ecological Processes (pp. 21–58). Oxford University Press.
Batty, M., & Jiang, B. (1999). Multi-agent simulation: new approaches to exploring space-time dynamics in GIS (CASA Working Papers 10). Centre for Advanced Spatial Analysis.
Bellifemine, F., Caire, G., & Greenwood, D. (2007). Developing Multi-agent Systems with JADE. Hoboken Wiley. https://doi.org/10.1002/9780470058411
Bodea, C., & Mogos, R. I. (2007). An Electronic Market Space Architecture Based on Intelligent Agents and Data Mining Technologies. Informatică Economică, 11(4), 115–118. https://revistaie.ase.ro/content/44/24%20bodea%20c.pdf
Braubach, L., Pokahr, A., & Lamersdorf, W. (2005). Jadex: a BDI-agent system combining middleware and reasoning. In R. Unland, M. Calisti, & M. Klusch (Eds.), Software Agent-Based Applications, Platforms and Development Kits (pp. 143–168). Birkhäuser Basel. https://doi.org/10.1007/3-7643-7348-2_7
Campbell, A., Pham, B., & Tian, Y.-C. (2005). A Delay-Embedding Approachto Multi-Agent System Construction and Calibration. In A. Zerger & R. M. Argent (Eds.), Proceedings MODSIM 2005 International Congress on Modelling and Simulation (pp. 113–119). Modelling and Simulation Society of Australia and New Zealand.
Canito A., Santos, G., Corchado, J. M., Marreiros, G., Vale, Z. (2019). Semantic Web Services for Multi-agent Systems Interoperability. In P. Moura Oliveira, P. Novais, & L. Reis (Eds.), EPIA 2019: Progress in Artificial Intelligence (pp. 606–616). Springer. https://doi.org/10.1007/978-3-030-30244-3_50
Carneiro, J., Alves, P., Marreiros, G., & Novais, P. (2019). A Multi-agent system framework for dialogue games in the group decision-making context. In Á. Rocha, H. Adeli, L. Reis, & S. Costanzo (Eds.), WorldCIST'19 2019: New Knowledge in Information Systems and Technologies (pp. 437–447). https://doi.org/10.1007/978-3-030-16181-1_41
Cavedon, L., Maamar, Z., Martin, D. L., Benatallah, B. (2004). Agent-Based Support for Service Composition. In L. Cavedon, Z. Maamar, D. Martin, & B. Benatallah (Eds.), Extending Web Services Technologies: The Use of Multi-agent Approaches (pp. 139–160). Springer. https://doi.org/10.1007/b101301
Chen, C.-C., Thakkar, S., Knoblock, C., & Shahabi, C. (2003). Automatically Annotating and Integrating Spatial Datasets. In T. Hadzilacos, Y. Manolopoulos, J. Roddick, & Y. Theodoridis (Eds.), SSTD 2003: Advances in Spatial and Temporal Databases (pp. 469–488). Springer. https://doi.org/10.1007/978-3-540-45072-6_27
Chen, F., Yang, H., Guo, H., & Xu, B. (2004). Agentification for Web Services. In Proceedings of the 28th Annual International Computer Software and Applications Conference (pp. 514–519). IEEE Computer Society Press.
Cotfas, L., Diosteanu, A., & Smeureanu, I. (2010). Agent-based Collaborative Manufacturing. In V. Luzar-Stiffler, I. Jarec, & Z. Bekic (Eds.), Proceedings of the ITI 2010, 32nd International Conference on Information Technology Interfaces (pp. 163–168). University Computing Centre, University of Zagreb.
Dioşteanu, A., & Cotfas, L. (2009). Agent Based Knowledge Management Solution using Ontology, Semantic Web Services and GIS. Informatica Economică, 13(4), 90–98. https://revistaie.ase.ro/content/52/09%20-%20Diosteanu,%20Cotfas.pdf
Fox, M., Barbuceanu, M., & Teigen, R. (2000). Agent-Oriented Supply-Chain Management. International Journal of Flexible Manufacturing Systems, 12, 165–188. https://doi.org/10.1023/A:1008195614074
Ferreira Filho, O. F., & Ferreira, M. A. G. V. (2009). Semantic Web Services: A Restful Approach. In P. Isaías, B. White, & M. B. Nunes (Eds.), IADIS International Conference WWW/Internet 2009 (pp. 169–180). Rome, Italy. https://www.iadisportal.org/digital-library/mdownload/semantic-web-services-a-restful-approach
Greenwood, D., & Calisti, M. (2004). Engineering Web service – agent integration. In 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583) (pp. 1918–1925). IEEE. https://doi.org/10.1109/ICSMC.2004.1399962
Gunasekera, K., Zaslavsky, A., Krishnaswamy, S., & Loke, S. W. (2010). Service Oriented Context-Aware Software Agents for Greater Efficiency. In P. Jędrzejowicz, N. T. Nguyen, R. J. Howlet, & L. C. Jain, (Eds.), KES-AMSTA 2010: Agent and Multi-Agent Systems: Technologies and Applications (pp. 62–71). Springer. https://doi.org/10.1007/978-3-642-13480-7_8
Huhns, M. N. (2002). Agents as Web services. IEEE Internet Computing, 6(4), 93–95. https://doi.org/10.1109/MIC.2002.1020332
Klügl, F., Herrler, R., & Fehler, M. (2006). SeSAm: implementation of agent-based simulation using visual programming. In AAMAS '06: Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems (pp. 1439–1440). Association for Computing Machinery. https://doi.org/10.1145/1160633.1160904
Klusch, M., Fries, B., & Sycara, K. (2006). Automated semantic web service discovery with OWLS-MX. In AAMAS '06: Proceedings of the Fifth International Joint Conference on Autonomous Agents and Multiagent Systems (pp. 915–922). https://doi.org/10.1145/1160633.1160796
Klusch, M., Kapahnke, P., Schulte, S., Lecue, F., & Bernstein, A. (2016). Semantic Web Service Search: A Brief Survey. KI – Künstliche Intelligenz, 30, 139–147. https://doi.org/10.1007/s13218-015-0415-7
Kopecký, J., Gomadam, K., & Vitvar, T. (2008). hRESTS: An HTML Microformat for Describing RESTful Web Services. In IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (pp. 619–625). IEEE. https://doi.org/10.1109/WIIAT.2008.379
Kravari, K., Bassiliades, N., & Boley, H. (2012). Cross-community interoperation between knowledge-based Multi-agent systems: A study on EMERALD and rule responder. Expert Systems with Applications, 39(10), 9571–9587. https://doi.org/10.1016/j.eswa.2012.02.160
Kravari, K., Kontopoulos, E., & Bassiliades, N. (2010). EMERALD: A Multi-Agent System for Knowledge-Based Reasoning Interoperability in the Semantic Web. In S. Konstantopoulos, S. Perantonis, V. Karkaletsis, C. D. Spyropoulos, & G. Vouros (Eds.), SETN 2010: Artificial Intelligence: Theories, Models and Applications (pp. 173–182). Springer. https://doi.org/10.1007/978-3-642-12842-4_21
Lemos, A. L., Daniel, F., & Benatallah, B. (2015). Web service composition. ACM Computing Surveys, 48(3), Article 33. https://doi.org/10.1145/2831270
Lord, P., Alper, P., Wroe, C., & Goble, C. (2005). Feta: A Light-Weight Architecture for User Oriented Semantic Service Discovery. In A. Gómez-Pérez & J. Euzena (Eds.), ESWC 2005: The Semantic Web: Research and Applications (pp. 17–31). https://doi.org/10.1007/11431053_2
Martin, D., Burstein, M., McIlraith, S., Paolucci, M., & Sycara, K. (2004). OWL-S and Agent-Based Systems. In L. Cavedon, Z. Maamar, D. Martin, & B. Benatallah (Eds.), Extending Web Services Technologies (pp. 53−77). Springer. https://doi.org/10.1007/0-387-23344-X_3
Mcllraith, S. A., Son, T. C., & Zeng, H. (2001). Semantic Web services. IEEE Intelligent Systems, 16(2), 46–53. https://doi.org/10.1109/5254.920599
Pedrinaci, C., Cardoso, J., & Leidig, T. (2014). Linked USDL: A Vocabulary for Web-Scale Service Trading. In V. Presutti, C. d’Amato, F. Gandon, M. d’Aquin, S. Staab, & A. Tordai (Eds.), ESWC 2014: The Semantic Web: Trends and Challenges (pp. 68–82). https://doi.org/10.1007/978-3-319-07443-6_6
Pinto, T., Morais, H., Sousa, T. M., Sousa, T., Vale, Z., Praça, I., Faia, R., & Pires, E. J. S. (2016). Adaptive portfolio optimization for multiple electricity markets participation. IEEE Transactions on Neural Networks and Learning Systems, 27(8), 1720–1733. https://doi.org/10.1109/TNNLS.2015.2461491
Pinto, T., Vale, Z., Praça, I., Pires E. J. S, & Lopes, F. (2015). Decision Support for Energy Contracts Negotiation with Game Theory and Adaptive Learning. Energies, 8(9), 9817–9842. https://doi.org/10.3390/en8099817
Rodriguez-Mier, P., Pedrinaci, C., Lama, M., & Mucientes, M. (2016). An Integrated Semantic Web Service Discovery and Composition Framework. IEEE Transactions on Services Computing, 9(4), 537–550. https://doi.org/10.1109/TSC.2015.2402679
Santos, G., Femandes, F., Pinto, T., Silva, M., Abrishambaf, O., Morais, H., & Vale, Z. (2016, October 19–21). House management system with real and virtual resources: Energy efficiency in residential microgrid. Global Information Infrastructure Symposium (GIIS 2016), Porto, Portugal. https://ieeexplore.ieee.org/document/7814943
Santos, G., Pinto, T., Praça, I., & Vale, Z. (2016). MASCEM: Optimizing the performance of a multi-agent system. Energy, 111, 513–524. https://doi.org/10.1016/j.energy.2016.05.127
Shen, W., Hao, Q., Wang, S., Li, Y., & Ghenniwa, H. (2007). An agent-based service-oriented integration architecture for collaborative intelligent manufacturing. Robotics and Computer-Integrated Manufacturing, 23(3), 315–325. https://doi.org/10.1016/j.rcim.2006.02.009
Smeureanu, I., Ruxanda, G., Diosteanu, A., Delcea, C., & Cotfas, L. A. (2012). Intelligent Agents and Risk Based Model for Supply Chain Management. Technological and Economic Development, 18(3), 452–469. https://doi.org/10.3846/20294913.2012.702696
State Statistics Service of Ukraine. (2022). https://www.ukrstat.gov.ua
Taillandier, P., Duc-An, V., Amouroux, E., & Drogoul, A. (2010). GAMA: Bringing GIS and multi-level capabilities to Multi-agent simulation. European Workshop on Multi-Agent Systems, 2010, Paris, France. https://hal.science/hal-00691400
Teixeira, B., Pinto, T., Silva, F., Santos, G., Praça, I., & Vale, Z. (2018). Multi-agent decision support tool to enable interoperability among heterogeneous energy systems. Applied Sciences, 8(3), Article 328. https://doi.org/10.3390/app8030328
Teixeira, B., Silva, F., Pinto, T., Santos, G., Praca, I., & Vale, Z. (2017). TOOCC: Enabling heterogeneous systems interoperability in the study of energy systems. IEEE Power and Energy Society General Meeting. Chicago, Illinois, USA. https://doi.org/10.1109/PESGM.2017.8274338
Valjarević, A., Valjarević, D., Stanojević-Ristić, Z., Djekić, T., & Živić, N. (2018). A geographical information systems-based approach to health facilities and urban traffic system in Belgrade, Serbia. Geospatial Health, 13(2), 308–313. https://doi.org/10.4081/gh.2018.729
World Health Organization. (2022). https://www.who.int/
Xiao, T., Mu, T., Shen, S., Song, Y., Yang, S., & He, J. (2022). A dynamic physical-distancing model to evaluate spatial measures for prevention of Covid-19 spread. Physica A: Statistical Mechanics and its Applications, 592, Article 126734. https://doi.org/10.1016/j.physa.2021.126734
Yu, G., Di, L., Yang, W., Zhao, P., & Yue P. (2009). Multi-agent Systems for Distributed Geospatial Modeling. Simulation and Computing. In H. A. Karimi (Ed.), Handbook of Research on Geoinformatics (pp. 196–204). IGI Global. https://doi.org/10.4018/978-1-59140-995-3.ch025
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