An Agent-Based Pedestrian and Group Dynamics Model Applied to Experimental and Real-World Scenarios

REFERENCES

• Bandini, S., Manzoni, S., & Vizzari, G. (2004). Situated cellular agents: A model to simulate crowding dynamics. IEICE Transactions on Information and Systems: Special Issues on Cellular Automata, E87-D(3), 669–676.
   Google Scholar
• Bandini, S., Manzoni, S., & Vizzari, G. (2009). Agent based modeling and simulation: An informatics perspective. Journal of Artificial Societies and Social Simulation, 12(4), 4.
   Web of Science ®Google Scholar
• Bandini, S., Rubagotti, F., Vizzari, G., & Shimura, K. (2011). An agent model of pedestrian and group dynamics: Experiments on group cohesion. In R. Pirrone & F. Sorbello (Eds.), AI*IA, Lecture Notes in Computer Science, 6934, 104–116.
   Google Scholar
• Batty, M. (2001). Agent based pedestrian modeling [Editorial]. Environment and Planning B: Planning and Design, 28, 321–326.
   Web of Science ®Google Scholar
• Blue, V. J., & Adler, J. L. (1999). Cellular automata microsimulation of bi-directional pedestrian flows. Transportation Research Record, 1678, 135–141.
   Google Scholar
• Bonomi, A., Manenti, L., Manzoni, S., & Vizzari, G. (2011). Makksim: Dealing with pedestrian groups in mas-based crowd simulation. In G. Fortino, A. Garro, L. Palopoli, W. Russo, & G. Spezzano (Eds.), WOA, CEUR Workshop Proceedings, 741, 166–170. CEUR-WS.org
   Google Scholar
• Castle, C., Waterson, N., Pellissier, E., & Bail, S. (2011). A comparison of grid-based and continuous space pedestrian modelling software: Analysis of two UK train stations. In R. D. Peacock, E. D. Kuligowski, & J. D. Averill (Eds.), Pedestrian and evacuation dynamics (pp. 433–446). New York, NY: Springer US.
   Google Scholar
• Challenger, R., Clegg, C. W., & Robinson, M. A. (2009). Understanding crowd behaviours: Supporting evidence. Technical report, University of Leeds, Leeds, UK.
   Google Scholar
• Ezaki, T., Yanagisawa, D., & Nishinari, K. (2012, August). Pedestrian flow through multiple bottlenecks. Physics Review E, 86, 026118.
   Web of Science ®Google Scholar
• Fruin, J. J. (1971). Pedestrian planning and design. New York, NY: Metropolitan Association of Urban Designers and Environmental Planners.
   Google Scholar
• Hall, E. T. (1966). The hidden dimension. New York, NY: Anchor Books.
   Google Scholar
• Helbing, D., & Molnár, P. (1995, May). Social force model for pedestrian dynamics. Physics Review E, 51(5), 4282–4286.
   PubMed Web of Science ®Google Scholar
• Henein, C. M., & White, T. (2005). Agent-based modelling of forces in crowds. In P. Davidsson, B. Logan, & K. Takadama (Eds.), Multi-agent and multi-agent-based simulation, Joint workshop MABS 2004, New York, NY, USA, July 19, 2004, Revised selected papers. Lecture Notes in Computer Science, 3415, 173–184.
   Google Scholar
• Kirchner, A., Nishinari, K., & Schadschneider, A. (2003). Friction effects and clogging in a cellular automaton model for pedestrian dynamics. Physical Review E, 67(5).
   PubMed Web of Science ®Google Scholar
• Kirchner, A., & Schadschneider, A. (2002). Simulation of evacuation processes using a bionics-inspired cellular automaton model for pedestrian dynamics. Physica A: Statistical Mechanics and its Applications, 312(1–2), 260–276.
   Web of Science ®Google Scholar
• Klügl, F., Fehler, M., & Herrler, R. (2004). About the role of the environment in multi-agent simulations. Danny Weyns, H. Van Dyke Parunak, Fabien Michel (Eds.), Environments for Multi-Agent Systems, First International Workshop, E4MAS 2004, New York, NY, USA, 127–149.
   Google Scholar
• Klüpfel, H. (2003). A cellular automaton model for crowd movement and egress simulation. PhD thesis, University of Duisburg-Essen, Essen, Germany.
   Google Scholar
• Manenti, L., Manzoni, S., Vizzari, G., Ohtsuka, K., & Shimura, K. (2010). Towards an agent-based proxemic model for pedestrian and group dynamic. In A. Omicini & M. Viroli (Eds.), WOA, CEUR Workshop Proceedings, 621. CEUR-WS.org
   Google Scholar
• Manzoni, S., Vizzari, G., Ohtsuka, K., & Shimura, K. (2011). Towards an agent-based proxemic model for pedestrian and group dynamics: Motivations and first experiments. In K. Tumer, P. Yolum, L. Sonenberg, & P. Stone (Eds.), Proceedings of 10th International Conference on Autonomous Agents and Multiagent Systems—Innovative applications track (AAMAS 2011), 1223–1224.
   Google Scholar
• Mori, M., & Tsukaguchi, H. (1987). A new method for evaluation of level of service in pedestrian facilities. Transportation Research Part A, 21(3), 223–234.
   Google Scholar
• Moussaïd, M., Perozo, N., Garnier, S., Helbing, D., & Theraulaz, G. (2010). The walking behaviour of pedestrian social groups and its impact on crowd dynamics. PLoS One, 5(4), e10047, 04.
   PubMed Web of Science ®Google Scholar
• Musse, S. R., & Thalmann, D. (2001). Hierarchical model for real time simulation of virtual human crowds. IEEE Transactions on Visualization and Comput Graphics 7(2), 152–164.
   Web of Science ®Google Scholar
• Nishinari, K., Kirchner, A., Namazi, A., & Schadschneider, A. (2004). Extended floor field ca model for evacuation dynamics. IEICE Transactions on information and systems, 87(3), 726–732.
   Google Scholar
• Nishinari, K., Suma, Y., Yanagisawa, D., Tomoeda, A., Kimura, A., & Nishi, R. (2008). Toward smooth movement of crowds. In Pedestrian and evacuation dynamics 2008 (pp. 293–308). Berlin, Germany: Springer.
   Google Scholar
• Paris, S., & Donikian, S. (2009). Activity-driven populace: A cognitive approach to crowd simulation. IEEE Computer Graphics and Applications, 29(4), 34–43.
   PubMed Web of Science ®Google Scholar
• Qiu, F., & Hu, X. (2010). Modeling group structures in pedestrian crowd simulation. Simulation Modelling Practice and Theory, 18(2), 190–205.
   Web of Science ®Google Scholar
• Rodrigues, R. A., de Lima Bicho, A., Paravisi, M., Jung, C. R., Magalhães, L. P., & Musse, S. R. (2010). An interactive model for steering behaviors of groups of characters. Applied Artificial Intelligence, 24(6), 594–616.
   Web of Science ®Google Scholar
• Sarmady, S., Haron, F., & Talib, A. Z. H. (2009). Modeling groups of pedestrians in least effort crowd movements using cellular automata. In D. Al-Dabass, R. Triweko, S. Susanto, & A. Abraham (Eds.), Asia international conference on modelling and simulation (pp. 520–525). Los Alamitos, CA: IEEE Computer Society.
   Google Scholar
• Schadschneider, A., Kirchner, A., & Nishinari, K. (2002). Ca approach to collective phenomena in pedestrian dynamics. In S. Bandini, B. Chopard, & M. Tomassini (Eds.), Cellular automata, 5th International conference on cellular automata for research and industry, ACRI 2002. Lecture Notes in Computer Science, 2493, 239–248.
   Google Scholar
• Schadschneider, A., Klingsch, W., Klüpfel, H., Kretz, T., Rogsch, C., & Seyfried, A. (2009) Evacuation dynamics: Empirical results, modeling and applications. In R. A. Meyers (Ed.), Encyclopedia of complexity and systems science (pp. 3142–3176). New York, NY: Springer.
   Google Scholar
• Schreckenberg, M., & Sharma, S. D. (Eds.). (2001). Pedestrian and evacuation dynamics. Berlin, Germany: Springer–Verlag.
   Google Scholar
• Shao, W., & Terzopoulos, D. (2007). Autonomous pedestrians. Graphical Models, 69(5–6), 246–274.
   Web of Science ®Google Scholar
• Singh, H., Arter, R., Dodd, L., Langston, P., Lester, E., & Drury, J. (2009). Modelling subgroup behaviour in crowd dynamics DEM simulation. Applied Mathematical Modelling, 33, 4408–4423.
   Web of Science ®Google Scholar
• Tsai, J., Fridman, N., Bowring, E., Brown, M., Epstein, S., Kaminka, G. A., … Tambe, M. (2011). Escapes—Evacuation simulation with children, authorities, parents, emotions, and social comparison. In K. Tumer, P. Yolum, L. Sonenberg, & P. Stone (Eds.), Proceedings of 10th International Conference on Autonomous Agents and Multiagent Systems—Innovative applications track (AAMAS 2011), 457–464.
   Google Scholar
• Vizzari, G., Manenti, L., and Crociani, L. (2013). Adaptive pedestrian behaviour for the preservation of group cohesion. Complex Adaptive Systems Modeling, 1(7).
   Google Scholar
• Was, J. (2010). Crowd dynamics modeling in the light of proxemic theories. In L. Rutkowski, R. Scherer, R. Tadeusiewicz, L. A. Zadeh, & J. M. Zurada (Eds.), ICAISC (2). Lecture Notes in Computer Science, 6114, 683–688.
   Google Scholar
• Weidmann, U. (1993). Transporttechnik der fussgänger—transporttechnische eigenschaftendes fussgängerverkehrs (literaturstudie). Zürich, Switzerland: Literature Research 90, Institut füer Verkehrsplanung, Transporttechnik, Strassen- und Eisenbahnbau IVT an der ETH Zürich.
   Google Scholar
• Willis, A., Gjersoe, N., Havard, C., Kerridge, J., & Kukla, R. (2004). Human movement behaviour in urban spaces: Implications for the design and modelling of effective pedestrian environments. Environment and Planning B, 31(6):805–828, 2004.
   Web of Science ®Google Scholar
• Xu, S., & Duh, H. B.-L. (2010). A simulation of bonding effects and their impacts on pedestrian dynamics. IEEE Transactions on Intelligent Transportation Systems, 11(1), 153–161.
   Web of Science ®Google Scholar