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

Abstract

Small-scale spatial events are situations in which elements or objects vary in such a way that temporal dynamics is 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 issue are key features of such events. 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 an unobstructed fashion to the event. Congestion and problems over safety are then resolved through introducing controls in an iterative fashion and 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 this is intrinsic to the simulation. AcknowledgmentsThe ESRC Nexsus Project (L326-25-3048) provided partial support for the project.

Self-Organized Pedestrian Crowd Dynamics: Experiments, Simulations, and Design Solutions

“…Moreover, by parallel simulation of the social force model on parallel PC clusters, it becomes possible to evaluate mass events, airport terminals, railway stations, and stadia in advance. Nowadays, one can also simulate pedestrian flows in extended urban areas (Batty, Desyllas, and Duxbury 2003;Helbing, Johansson, and Buzna 2004). This allows one to assess the attractiveness of certain locations for new shops, but also the impact of new buildings like theaters or malls on the overall pedestrian flows.…”

Computational Streetscapes

“…(This conceptualization of look-up time actually fits well with the emerging idea that human brains may simulate the physics of interaction and timing in the visual scenes they detect before decisions are made [364]) Still other schemes decouple timing, segmenting action between long-run tasks such as trip-planning, and short-term needs such as collision avoidance [65]. Approaches developed in urban studies typically follow an activity space approach, whereby models begin a simulation by releasing modeled people in particular places and times to engage in various behaviors appropriate to that time geography [365][366][367]. Some really innovative work has been done to accomplish this for historical streetscapes [368,369], to put characters in the right place, time, and context for ancient Roman environments, for example [370].…”

Decentralized Movement Pattern Detection amongst Mobile Geosensor Nodes

“…The token (X, j) is updated by X := X ∩ nbr (a, t i ) to indicate that now only a smaller set of sensor nodes are in a's neighborhood, and by j := j + 1 to reflect the increased number of time steps that the sensor nodes in the updated X have been neighbors of a (line 6). If the size of the updated X is at least the number of sensor nodes required for detecting a flock ν, the token is added to TokenSet(a, t i ) (lines [7][8]. After that, the sensor node a inspects all tokens in TokenSet(a, t i ), and whenever it finds a token of age j ≥ k it triggers a "pattern found" message (lines 9-11).…”

“…They are the spatiotemporal "trace" left behind by the behavior of moving entities [2]. Examples of movement patterns include flocking as in a "mob" of sheep [3], leading and following found in group dynamics [4,5], or converging and diverging of pedestrians in crowding scenarios [6,7]. Figure 1 illustrates the movement pattern of a prototypical "flock".…”