Research Article
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Bulanık Mantık ile Kullanıcı Hareketlerinde Etmen Tabanlı Modelleme

Year 2021, Volume: 2 Issue: 1, 243 - 264, 31.03.2021

Berfin Yıldız , Gülen Çağdaş

Abstract

Tasarım süreçlerinin artan karmaşıklığı, tasarımcı ile kullanıcı arasındaki mesafeyi arttırmakta ve bu da tasarımda kullanıcı deneyimini göz önünde bulundurmayı zorlaştırmaktadır. Kullanıcıların otonom karar verme varlıkları olarak temsil edildiği hesaplamalı modeller kullanıcı davranışlarının benzetim modelleri ile temsil edilmesinde yardımcı olmaktadır. Bu bağlamda, modellerin geliştirilmesi kentsel tasarımın erken aşamasında karar vermeyi desteklemektedir. Bu çalışmanın amacı, kullanıcının kentsel mekâna nasıl dahil olduğunu araştırmak ve kullanıcı hareketinin benzetimi için bir model geliştirebilmek ve kentsel alan bileşenleri ile kullanıcıların hareketi arasındaki ilişkiyi analiz etmektir. Bu makale beş aşamalı ardışık bir süreci takip etmektedir: Gözlem çalışmaları ve çevresel analiz ile veri toplama; Verilerin bulanık mantık kullanarak yorumlanması; Etmen tabanlı model geliştirme; Model uygulama; Değerlendirme ve doğrulama. Gözlem verilerinin yorumlanması, kentsel mekân bileşenlerinin etki değerlerini bulanık mantıkla hesaplama süreçleridir. Bu değer, daha sonra etmen tabanlı benzetim modelinde etki kuvveti olarak tanımlanır. Benzetim modeli sonuçları, gözlem çıktıları ile karşılaştırmalı olarak değerlendirilmektedir. Örnek vaka incelemesi olarak, bir kentsel meydan seçilmiştir (Konak Meydanı, İzmir, Türkiye). Sabah ve akşam zaman dilimleri için iki model tanımlanmış ve meydandaki kullanıcı hareketinin benzetimi için test edilmiştir. Daha sonra model sonuçları ve gözlem verileri Ortalama Mutlak Yüzde Hata (Mean Absolute Percentage Error - MAPE) ve Sekant Kosinüs Hesaplama yöntemleri ile karşılaştırılarak modelin verimliliği incelenmiştir.

Keywords

Etmen tabanlı modelleme bulanık mantık kullanıcı hareketleri benzetim

References

  • Andresen, E., Haensel, D., Chraibi, M., & Seyfried, A. (2016). Wayfinding and cognitive maps for pedestrian models. Traffic and Granular Flow '15, 249-256. https://doi.org/10.1007/978-3-319-33482-0_32
  • Batty, M. (2001). Agent-based pedestrian modeling. SAGE Publications. https://doi.org/10.1068/b2803ed
  • Becker-Asano, C., Ruzzoli, F., Hölscher, C., & Nebel, B. (2014). A Multi-Agent System based on Unity 4 for virtual perception and wayfinding. Transportation Research Procedia, 2, 452–455. https://doi.org/10.1016/j.trpro.2014.09.059
  • Bradshaw, C. (1993). Creating—and using—a rating system for neighborhood walkability: towards an agenda for “local heroes.” 14th Intl Pedestrian Conf.
  • Chen, C.-H. (2009). A Prototype Using Multi-Agent Based Simulation in Spatial Analysis and Planning. The 14th Annual Conference of the Association of Computer Aided Architectural Design. CAADRIA.
  • Crawley, D. B., Lawrie, L. K., Pedersen, C. O., & Winkelmann, F. C. (2000). Energy plus: energy simulation program. ASHRAE Journal, 42(4), 49–56.
  • Drogoul, A., & Ferber, J. (1995). Multi-agent simulation as a tool for analysing emergent processes in societies. Proceedings of Simulating Societies Symposium.
  • Gilbert, N., & Doran, J. (1994). Simulating societies: the computer simulation of social phenomena. Routledge. https://doi.org/10.4324/9781351165129-6
  • Gehl, J. (2011). Life between buildings: using public space. Island Press.
  • Helbing, D. (1998). A fluid dynamic model for the movement of pedestrians. ArXiv Preprint Cond-Mat/9805213.
  • Helbing, D., Farkas, I. J., Molnar, P., & Vicsek, T. (2002). Simulation of pedestrian crowds in normal and evacuation situations. Pedestrian and Evacuation Dynamics, 21(2), 21–58.
  • Helbing, D., & Molnar, P. (1995). Social force model for pedestrian dynamics. Physical Review E, 51(5), 4282. https://doi.org/10.1103/physreve.51.4282
  • Helbing, D., Molnár, P., Farkas, I. J., & Bolay, K. (2001). Self-organizing pedestrian movement. Environment and Planning B: Planning and Design, 28(3), 361–383. https://doi.org/10.1068/b2697
  • Heliövaara, S., Korhonen, T., Hostikka, S., & Ehtamo, H. (2012). Counterflow model for agent-based simulation of crowd dynamics. Building and Environment, 48, 89–100. https://doi.org/10.1016/j.buildenv.2011.08.020
  • Hollmann C. (2015). A cognitive human behaviour model for pedestrian behaviour simulation [Doctoral dissertation]. https://gala.gre.ac.uk/id/eprint/13831/1/Claudia_Hollmann_2015.pdf
  • Jiang, B. (1999). SimPed: simulating pedestrian flows in a virtual urban environment. Journal of Geographic Information and Decision Analysis, 3(1), 21–30.
  • Johansson, F. (2013). Microscopic modeling and simulation of pedestrian traffic. Linköping University Electronic Press. https://doi.org/10.3384/lic.diva-101085
  • Khair, U., Fahmi, H., Al Hakim, S., & Rahim, R. (2017). Forecasting error calculation with mean absolute deviation and mean absolute percentage error. Journal of Physics: Conference Series, 930(1), 12002. IOP Publishing. https://doi.org/10.1088/1742-6596/930/1/012002
  • Kormanová, A. (2013). A review on macroscopic pedestrian flow modelling. Acta Informatica Pragensia, 2(2), 39–50. https://doi.org/10.18267/j.aip.22
  • Lewin, K. (1951). Field theory in social science: selected theoretical papers. (D. Cartwright, Ed.). Harper. https://doi.org/10.1086/638467
  • Luo, L., Zhou, S., Cai, W., Low, M. Y. H., Tian, F., Wang, Y., … Chen, D. (2008). Agent‐based human behavior modeling for crowd simulation. Computer Animation and Virtual Worlds, 19(3‐4), 271–281. https://doi.org/10.1002/cav.238
  • Mamdani, E. H., & Assilian, S. (1975). An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies, 7(1), 1–13. https://doi.org/10.1016/b978-1-4832-1450-4.50032-8
  • Narimatsu, K., Shiraishi, T., & Morishita, S. (2004). Acquisition of local neighbor rules in the simulation of pedestrian flow by cellular automata. International Conference on Cellular Automata, 211–219. Springer. https://doi.org/10.1007/978-3-540-30479-1_22
  • Padgham, L., & Winikoff, M. (2005). Developing intelligent agent systems: A practical guide (Vol. 13). John Wiley & Sons. https://doi.org/10.1002/0470861223
  • Peacock, R. D., Reneke, P. A., Davis, W. D., & Jones, W. W. (1999). Quantifying fire model evaluation using functional analysis. Fire Safety Journal, 33(3), 167–184. https://doi.org/10.1016/s0379-7112(99)00029-6
  • Pelechano, N., & Malkawi, A. (2008). Evacuation simulation models: Challenges in modeling high rise building evacuation with cellular automata approaches. Automation in Construction, 17(4), 377–385. https://doi.org/10.1016/j.autcon.2007.06.005
  • Puusepp, R., Cerrone, D., & Melioranski, M. (2016). Synthetic Modelling of Pedestrian Movement-Tallinn case study report [Paper presentation]. 34th eCAADe Conference, Finland.
  • Rahm, J., & Johansson, M. (2018). Assessing the pedestrian response to urban outdoor lighting: A full-scale laboratory study. PLoS One, 13(10), e0204638. https://doi.org/10.1371/journal.pone.0204638
  • Raubal, M. (2001). Human wayfinding in unfamiliar buildings: a simulation with a cognizing agent. Cognitive Processing, 2(3), 363–388.
  • Reynolds, C. W. (1999). Steering behaviors for autonomous characters. Game Developers Conference, 1999, 763–782. Citeseer.
  • Ronchi, E., Kuligowski, E. D., Reneke, P. A., Peacock, R. D., & Nilsson, D. (2013). The process of verification and validation of building fire evacuation models. US Department of Commerce, National Institute of Standards and Technology. https://doi.org/10.6028/nist.tn.1822
  • Rose, J., Ligtenberg, A., & Spek, S. van der. (2014). Simulating pedestrians through the inner-city: an agent-based approach. Social Simulation Conference.
  • Scheflen, A. E., & Ashcraft, N. (1976). Human territories: How we behave in space-time. https://doi.org/10.1111/j.1545-5300.1976.447_5_5.x
  • Schelhorn, T., O’Sullivan, D., Haklay, M., & Thurstain-Goodwin, M. (1999). STREETS: An agent-based pedestrian model.
  • Sugeno, M. (1985). An introductory survey of fuzzy control. Information Sciences, 36(1–2), 59–83. https://doi.org/10.1016/0020-0255(85)90026-x
  • Tavares, R. M., & Galea, E. R. (2009). Evacuation modelling analysis within the operational research context: A combined approach for improving enclosure designs. Building and Environment, 44(5), 1005–1016. https://doi.org/10.1016/j.buildenv.2008.07.019
  • Tisue, S., & Wilensky, U. (2004). Netlogo: A simple environment for modeling complexity. International Conference on Complex Systems, 21, 16–21. Boston, MA.
  • Waldau, N., Gattermann, P., Knoflacher, H., & Schreckenberg, M. (2007). Pedestrian and evacuation dynamics 2005 (Vol. 319). Springer. https://doi.org/10.1007/978-3-540-47064-9
  • Zadeh, L. A. (1999). Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems, 100(1), 9–34. https://doi.org/10.1016/s0165-0114(99)80004-9
  • Zheng, X., Zhong, T., & Liu, M. (2009). Modeling crowd evacuation of a building based on seven methodological approaches. Building and Environment, 44(3), 437–445. https://doi.org/10.1016/j.buildenv.2008.04.002

Year 2021, Volume: 2 Issue: 1, 243 - 264, 31.03.2021

Berfin Yıldız , Gülen Çağdaş

Abstract

References

  • Andresen, E., Haensel, D., Chraibi, M., & Seyfried, A. (2016). Wayfinding and cognitive maps for pedestrian models. Traffic and Granular Flow '15, 249-256. https://doi.org/10.1007/978-3-319-33482-0_32
  • Batty, M. (2001). Agent-based pedestrian modeling. SAGE Publications. https://doi.org/10.1068/b2803ed
  • Becker-Asano, C., Ruzzoli, F., Hölscher, C., & Nebel, B. (2014). A Multi-Agent System based on Unity 4 for virtual perception and wayfinding. Transportation Research Procedia, 2, 452–455. https://doi.org/10.1016/j.trpro.2014.09.059
  • Bradshaw, C. (1993). Creating—and using—a rating system for neighborhood walkability: towards an agenda for “local heroes.” 14th Intl Pedestrian Conf.
  • Chen, C.-H. (2009). A Prototype Using Multi-Agent Based Simulation in Spatial Analysis and Planning. The 14th Annual Conference of the Association of Computer Aided Architectural Design. CAADRIA.
  • Crawley, D. B., Lawrie, L. K., Pedersen, C. O., & Winkelmann, F. C. (2000). Energy plus: energy simulation program. ASHRAE Journal, 42(4), 49–56.
  • Drogoul, A., & Ferber, J. (1995). Multi-agent simulation as a tool for analysing emergent processes in societies. Proceedings of Simulating Societies Symposium.
  • Gilbert, N., & Doran, J. (1994). Simulating societies: the computer simulation of social phenomena. Routledge. https://doi.org/10.4324/9781351165129-6
  • Gehl, J. (2011). Life between buildings: using public space. Island Press.
  • Helbing, D. (1998). A fluid dynamic model for the movement of pedestrians. ArXiv Preprint Cond-Mat/9805213.
  • Helbing, D., Farkas, I. J., Molnar, P., & Vicsek, T. (2002). Simulation of pedestrian crowds in normal and evacuation situations. Pedestrian and Evacuation Dynamics, 21(2), 21–58.
  • Helbing, D., & Molnar, P. (1995). Social force model for pedestrian dynamics. Physical Review E, 51(5), 4282. https://doi.org/10.1103/physreve.51.4282
  • Helbing, D., Molnár, P., Farkas, I. J., & Bolay, K. (2001). Self-organizing pedestrian movement. Environment and Planning B: Planning and Design, 28(3), 361–383. https://doi.org/10.1068/b2697
  • Heliövaara, S., Korhonen, T., Hostikka, S., & Ehtamo, H. (2012). Counterflow model for agent-based simulation of crowd dynamics. Building and Environment, 48, 89–100. https://doi.org/10.1016/j.buildenv.2011.08.020
  • Hollmann C. (2015). A cognitive human behaviour model for pedestrian behaviour simulation [Doctoral dissertation]. https://gala.gre.ac.uk/id/eprint/13831/1/Claudia_Hollmann_2015.pdf
  • Jiang, B. (1999). SimPed: simulating pedestrian flows in a virtual urban environment. Journal of Geographic Information and Decision Analysis, 3(1), 21–30.
  • Johansson, F. (2013). Microscopic modeling and simulation of pedestrian traffic. Linköping University Electronic Press. https://doi.org/10.3384/lic.diva-101085
  • Khair, U., Fahmi, H., Al Hakim, S., & Rahim, R. (2017). Forecasting error calculation with mean absolute deviation and mean absolute percentage error. Journal of Physics: Conference Series, 930(1), 12002. IOP Publishing. https://doi.org/10.1088/1742-6596/930/1/012002
  • Kormanová, A. (2013). A review on macroscopic pedestrian flow modelling. Acta Informatica Pragensia, 2(2), 39–50. https://doi.org/10.18267/j.aip.22
  • Lewin, K. (1951). Field theory in social science: selected theoretical papers. (D. Cartwright, Ed.). Harper. https://doi.org/10.1086/638467
  • Luo, L., Zhou, S., Cai, W., Low, M. Y. H., Tian, F., Wang, Y., … Chen, D. (2008). Agent‐based human behavior modeling for crowd simulation. Computer Animation and Virtual Worlds, 19(3‐4), 271–281. https://doi.org/10.1002/cav.238
  • Mamdani, E. H., & Assilian, S. (1975). An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies, 7(1), 1–13. https://doi.org/10.1016/b978-1-4832-1450-4.50032-8
  • Narimatsu, K., Shiraishi, T., & Morishita, S. (2004). Acquisition of local neighbor rules in the simulation of pedestrian flow by cellular automata. International Conference on Cellular Automata, 211–219. Springer. https://doi.org/10.1007/978-3-540-30479-1_22
  • Padgham, L., & Winikoff, M. (2005). Developing intelligent agent systems: A practical guide (Vol. 13). John Wiley & Sons. https://doi.org/10.1002/0470861223
  • Peacock, R. D., Reneke, P. A., Davis, W. D., & Jones, W. W. (1999). Quantifying fire model evaluation using functional analysis. Fire Safety Journal, 33(3), 167–184. https://doi.org/10.1016/s0379-7112(99)00029-6
  • Pelechano, N., & Malkawi, A. (2008). Evacuation simulation models: Challenges in modeling high rise building evacuation with cellular automata approaches. Automation in Construction, 17(4), 377–385. https://doi.org/10.1016/j.autcon.2007.06.005
  • Puusepp, R., Cerrone, D., & Melioranski, M. (2016). Synthetic Modelling of Pedestrian Movement-Tallinn case study report [Paper presentation]. 34th eCAADe Conference, Finland.
  • Rahm, J., & Johansson, M. (2018). Assessing the pedestrian response to urban outdoor lighting: A full-scale laboratory study. PLoS One, 13(10), e0204638. https://doi.org/10.1371/journal.pone.0204638
  • Raubal, M. (2001). Human wayfinding in unfamiliar buildings: a simulation with a cognizing agent. Cognitive Processing, 2(3), 363–388.
  • Reynolds, C. W. (1999). Steering behaviors for autonomous characters. Game Developers Conference, 1999, 763–782. Citeseer.
  • Ronchi, E., Kuligowski, E. D., Reneke, P. A., Peacock, R. D., & Nilsson, D. (2013). The process of verification and validation of building fire evacuation models. US Department of Commerce, National Institute of Standards and Technology. https://doi.org/10.6028/nist.tn.1822
  • Rose, J., Ligtenberg, A., & Spek, S. van der. (2014). Simulating pedestrians through the inner-city: an agent-based approach. Social Simulation Conference.
  • Scheflen, A. E., & Ashcraft, N. (1976). Human territories: How we behave in space-time. https://doi.org/10.1111/j.1545-5300.1976.447_5_5.x
  • Schelhorn, T., O’Sullivan, D., Haklay, M., & Thurstain-Goodwin, M. (1999). STREETS: An agent-based pedestrian model.
  • Sugeno, M. (1985). An introductory survey of fuzzy control. Information Sciences, 36(1–2), 59–83. https://doi.org/10.1016/0020-0255(85)90026-x
  • Tavares, R. M., & Galea, E. R. (2009). Evacuation modelling analysis within the operational research context: A combined approach for improving enclosure designs. Building and Environment, 44(5), 1005–1016. https://doi.org/10.1016/j.buildenv.2008.07.019
  • Tisue, S., & Wilensky, U. (2004). Netlogo: A simple environment for modeling complexity. International Conference on Complex Systems, 21, 16–21. Boston, MA.
  • Waldau, N., Gattermann, P., Knoflacher, H., & Schreckenberg, M. (2007). Pedestrian and evacuation dynamics 2005 (Vol. 319). Springer. https://doi.org/10.1007/978-3-540-47064-9
  • Zadeh, L. A. (1999). Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems, 100(1), 9–34. https://doi.org/10.1016/s0165-0114(99)80004-9
  • Zheng, X., Zhong, T., & Liu, M. (2009). Modeling crowd evacuation of a building based on seven methodological approaches. Building and Environment, 44(3), 437–445. https://doi.org/10.1016/j.buildenv.2008.04.002
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Details

Primary Language Turkish
Subjects Architecture
Journal Section Research Articles
Authors

Berfin Yıldız

Gülen Çağdaş

Publication Date March 31, 2021
Published in Issue Year 2021 Volume: 2 Issue: 1

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APA Yıldız, B., & Çağdaş, G. (2021). Bulanık Mantık ile Kullanıcı Hareketlerinde Etmen Tabanlı Modelleme. Journal of Computational Design, 2(1), 243-264.
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