The discrete dynamics of small-scale spatial events: agent-based models of mobility in carnivals and street parades

Small-scale spatial events are situations in which elements or objects vary in such a way that temporal dynamics are intrinsic to their representation and explanation. Some of the clearest examples involve local movement, from conventional traffic modeling to disaster evacuation where congestion, crowding, panic, and related safety issues are key features. We propose that such events can be simulated using new variants of pedestrian model, which embody ideas about how behavior emerges from the accumulated interactions between small-scale objects. We present a model in which the event space is first explored by agents using ‘swarm intelligence’. Armed with information about the space, agents then move in unobstructed fashion to the event. Congestion and problems over safety are then resolved through introducing controls in an iterative fashion, rerunning the model until a ‘safe solution’ is reached. The model has been developed to simulate the effect of changing the route of the Notting Hill Carnival, an annual event held in west central London over 2 days in August each year. One of the key issues in using such simulation is how the process of modeling interacts with those who manage and control the event. As such, this changes the nature of the modeling problem from one where control and optimization is external to the model to one where it is intrinsic to the simulation.

[1]  A. Schadschneider,et al.  Simulation of pedestrian dynamics using a two dimensional cellular automaton , 2001 .

[2]  Frank Schweitzer Modelling Migration and Economic Agglomeration with Active Brownian Particles , 1998, Adv. Complex Syst..

[3]  B. Pushkarev URBAN SPACE FOR PEDESTRIANS , 1975 .

[4]  Michael Schreckenberg,et al.  A cellular automaton model for freeway traffic , 1992 .

[5]  Iain D. Couzin,et al.  Self‐Organization in Biological Systems.Princeton Studies in Complexity. ByScott Camazine,, Jean‐Louis Deneubourg,, Nigel R Franks,, James Sneyd,, Guy Theraulaz, and, Eric Bonabeau; original line drawings by, William Ristineand, Mary Ellen Didion; StarLogo programming by, William Thies. Princeton (N , 2002 .

[6]  Vicsek,et al.  Novel type of phase transition in a system of self-driven particles. , 1995, Physical review letters.

[7]  Frank Schweitzer,et al.  Active brownian particles: Artificial agents in physics , 1997 .

[8]  E. Canetti,et al.  Crowds and Power , 1960 .

[9]  Philip Steadman,et al.  The Geometry of Environment , 2020 .

[10]  Boris Pushkarev,et al.  Urban space for pedestrians : a report of the Regional Plan Association , 1975 .

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

[12]  John J. Fruin,et al.  Pedestrian planning and design , 1971 .

[13]  D. Helbing Traffic and related self-driven many-particle systems , 2000, cond-mat/0012229.

[14]  Michael Batty,et al.  Geographical Information Systems and Urban Design , 1999 .

[15]  M. Batty Polynucleated Urban Landscapes , 2001 .

[16]  D. Sornette Critical Phenomena in Natural Sciences: Chaos, Fractals, Selforganization and Disorder: Concepts and Tools , 2000 .

[17]  A E Baer A SIMULATION MODEL OF MULTIDIRECTIONAL PEDESTRIAN MOVEMENT WITHIN PHYSICALLY BOUNDED ENVIRONMENTS , 1974 .

[18]  L. F. Henderson,et al.  The Statistics of Crowd Fluids , 1971, Nature.

[19]  Victor J. Blue,et al.  Cellular automata microsimulation for modeling bi-directional pedestrian walkways , 2001 .

[20]  T. A. A. Broadbent,et al.  The Geometry of Environment. An Introduction to Spatial Organization in Design , 1972, The Mathematical Gazette.

[21]  Michael Batty,et al.  Modelling Inside GIS: Part 2. Selecting and Calibrating Urban Models Using ARC-INFO , 1994, Int. J. Geogr. Inf. Sci..

[22]  John Rannells,et al.  The Core of the City: A Pilot Study of Changing Land Uses in Central Business Districts. , 1956 .

[23]  J. M. Henderson,et al.  Microeconomic Theory: A Mathematical Approach. , 1959 .

[24]  Dirk Helbing,et al.  A mathematical model for the behavior of pedestrians , 1991, cond-mat/9805202.

[25]  Gunnar G. Løvås,et al.  Modeling and Simulation of Pedestrian Traffic Flow , 1994 .

[26]  Hjp Harry Timmermans,et al.  A Multi-Agent Cellular Automata Model of Pedestrian Movement , 2001 .

[27]  T. Vicsek,et al.  Simulation of pedestrian crowds in normal and evacuation situations , 2002 .

[28]  Guy Theraulaz,et al.  Self-Organization in Biological Systems , 2001, Princeton studies in complexity.

[29]  John Rannells,et al.  The Core of the City: A Pilot Study of Changing Land Uses in Central Business Districts. , 1957 .

[30]  Craig W. Reynolds Flocks, herds, and schools: a distributed behavioral model , 1987, SIGGRAPH.

[31]  D. Sornette Critical Phenomena in Natural Sciences: Chaos, Fractals, Selforganization and Disorder: Concepts and Tools , 2000 .

[32]  David O'Sullivan,et al.  “So Go Downtown”: Simulating Pedestrian Movement in Town Centres , 2001 .

[33]  Dirk Helbing,et al.  Self-Organizing Pedestrian Movement , 2001 .

[34]  Barbara Webb,et al.  Swarm Intelligence: From Natural to Artificial Systems , 2002, Connect. Sci..

[35]  Steven Johnson,et al.  Emergence: The Connected Lives of Ants, Brains, Cities, and Software , 2001 .