EMERGENCE OF A TRAFFIC FLOW CONVENTION IN A MULTIAGENT MODEL

We study the emergence of self-organized traffic flow conventions in a multiagent system. A transport system is modeled as a discrete grid, on which agents governed by a neural network can move with a velocity that depends on the quantity and directions of the agents occupying the site of the grid. We show that evolution leads to traffic flow conventions that depend on the transport system geometry. We also show that if we split the agents into different “species” that move simultaneously on the transport system but that cannot exchange genetic information, their mutual influence leads to the same traffic flow convention for all the species. Finally, we develop a very simple evolutionary dynamic model (solvable both numerically and analytically) that gives an interpretation of our results.