Author(s): M. Salatino
Pedestrian simulation is a central issue in evacuation related topics; an issue that
has recently received renewed interest.
In order to estimate escape time from a
building, this paper describes a two-module model which combines Agent-Based
Models (ABM) and Cellular Automata (CA).
The former module (ABM)
simulates pedestrians exploring the building space; the latter (CA) simulates the
proper evacuation process.
The novelty of the model is represented by the first
module’s approach, which is inspired to Ant Colony Optimisation (ACO).
this metaphor, it is possible to simulate the way in which people draw their
cognitive map of the building’s space.
According to ACO, agents represent
‘scout ants’ looking for the exit.
Initially, ants move in a random fashion.
an ant reaches the exit, it updates the grid by adding an amount of pheromone.
The result is a pheromone trail that follows the shortest possible path from anthill
to the exit cell.
Running the former module, we obtain a map containing
distances from each point to the exit.
The latter CA module uses this map to
estimate escape time.
Cellular Automata; evacuation processes; pedestrian behaviour.
Simulating pedestrian behaviour can be ascribed to problems dealing with
Everyone has experienced the complexity of pedestrian
dynamics: speed slowly decreases as crowding arises, then it drops to zero when
density equals a specific critical value.
Indeed, jamming formation is due to local
fluctuations in pedestrian speed.
According to Complex Systems Theory,
microscopic events may able to produce macroscopic behaviours, the so-called
We live through complex systems behaviour every day in
a traffic jam, when we stand in a queue or leave a crowded place.
Size: 660 kb
Paper DOI: 10.2495/SAFE050041
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