CODE: Crowd‐optimized design of environments

We present crowd‐optimized design of environments (CODE): a “crowd‐aware” computational tool for designing environments (e.g., building floor plans). Our system analyses the impact of newly added environment elements (e.g., pillars or doorways) on the resulting crowd flow, using current‐generation crowd simulators. The results of the simulation are used to provide feedback to the designer in terms of aggregate statistics and heat maps. Additionally, our system is able to “automatically” optimize the placement of environment elements to maximize crowd flow in egress scenarios, while satisfying constraints that are imposed by the designer. Using CODE, architects and environment designers can iteratively refine upon their original design to quickly accommodate the dynamic properties of crowd simulations in an interactive fashion. CODE is modular and flexible so that designers may build environments, select from different crowd simulators, and specify varying crowd configurations.

[1]  Dinesh Manocha,et al.  A statistical similarity measure for aggregate crowd dynamics , 2012, ACM Trans. Graph..

[2]  Chi-Keung Tang,et al.  Make it home: automatic optimization of furniture arrangement , 2011, ACM Trans. Graph..

[3]  Dirk Helbing,et al.  Pedestrian flow optimization with a genetic algorithm based on Boolean grids , 2007 .

[4]  Alban Bassuet,et al.  Computational and Optimization Design in Geometric Acoustics , 2014 .

[5]  Leslie K. Norford,et al.  A design optimization tool based on a genetic algorithm , 2002 .

[6]  Rudi Stouffs,et al.  Design explorations of performance driven geometry in architectural design using parametric modeling and genetic algorithms , 2011, Adv. Eng. Informatics.

[7]  Petros Faloutsos,et al.  Evaluating and optimizing level of service for crowd evacuations , 2015, MIG.

[8]  Dinesh Manocha,et al.  Menge: A Modular Framework for Simulating Crowd Movement , 2016 .

[10]  Jeremy J. Michalek,et al.  Interactive design optimization of architectural layouts , 2002 .

[11]  Yun Kyu Yi,et al.  Performance based architectural design optimization: Automated 3D space layout using simulated annealing , 2014 .

[12]  Glenn Reinman,et al.  An Open Framework for Developing, Evaluating, and Sharing Steering Algorithms , 2009, MIG.

[13]  Norman I. Badler,et al.  Virtual Crowds: Methods, Simulation, and Control , 2008, Virtual Crowds: Methods, Simulation, and Control.

[14]  Stéphane Donikian,et al.  Experiment-based modeling, simulation and validation of interactions between virtual walkers , 2009, SCA '09.

[15]  Glenn Reinman,et al.  A modular framework for adaptive agent-based steering , 2011, SI3D.

[16]  Zhangang Han,et al.  Obstacle Optimization for Panic Flow - Reducing the Tangential Momentum Increases the Escape Speed , 2014, PloS one.

[17]  P. Colella,et al.  Local adaptive mesh refinement for shock hydrodynamics , 1989 .

[18]  Dinesh Manocha,et al.  Reciprocal n-Body Collision Avoidance , 2011, ISRR.

[19]  Petros Faloutsos,et al.  Characterizing and optimizing game level difficulty , 2014, MIG 2014.

[20]  Wenjie Yang,et al.  Performance-driven architectural design and optimization technique from a perspective of architects , 2013 .

[21]  Dinesh Manocha,et al.  Parameter estimation and comparative evaluation of crowd simulations , 2014, Comput. Graph. Forum.

[22]  Johannes Wallner,et al.  Architectural geometry , 2007, Comput. Graph..

[23]  Lubos Buzna,et al.  Self-Organized Pedestrian Crowd Dynamics: Experiments, Simulations, and Design Solutions , 2005, Transp. Sci..

[24]  Dirk Helbing,et al.  Simulating dynamical features of escape panic , 2000, Nature.

[25]  Petros Faloutsos,et al.  Environment optimization for crowd evacuation , 2015, Comput. Animat. Virtual Worlds.

[26]  Vladlen Koltun,et al.  Computer-generated residential building layouts , 2010, SIGGRAPH 2010.

[27]  Donald H. House,et al.  Modeling architectural design objectives in physically based space planning , 2002 .

[28]  Petros Faloutsos,et al.  SteerFit: automated parameter fitting for steering algorithms , 2014, SCA '14.

[29]  Philippe Block,et al.  Advances in architectural geometry , 2010 .