Clustering Drivers' Travel Patterns for the Flock Navigation Traffic Coordination Model

Everyday people face road traffic congestions in big cities causing wastes in time, productivity and accidents. Several techniques from different fields in science and technology have been proposed for road management that deal with different ways of modeling traffic lights, policies, cars and coordination. Here we are restricting our attention to automated vehicles coordination approaches, which indeed make big assumptions on the technological infrastructure. Further, we follow an approach called "Flock Traffic Navigation", where vehicles group in "flocks", just like many animal species travel in nature, in order to increase efficiency and security. The present work deals with the use of the information about drivers' usual travel patterns. Clustering techniques are implemented based on that information to find ways of joining vehicles into flocks, receiving thus the advantages of Flock Traffic Navigation. We evaluate the advantages of grouping cars using the drivers' travel patterns and show the feasibility of our approach.