Author(s)

M. Salatino

Abstract

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). Using 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. When 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. Keywords: Cellular Automata; evacuation processes; pedestrian behaviour. 1 Introduction Simulating pedestrian behaviour can be ascribed to problems dealing with Complex Systems. 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 emergent phenomena. We live through complex systems behaviour every day in a traffic jam, when we stand in a queue or leave a crowded place.

Keywords

Cellular Automata; evacuation processes; pedestrian behaviour.