  We examine adaptive strategies adopted by vehicles for route selection
en-route in transportation networks. By studying a model of two-dimensional
cellular automata, we model vehicles characterized by a parameter called
path-greediness, which corresponds to the tendency for them to travel to their
destinations via the shortest path. The path-greediness of each individual
vehicle is updated based on the local traffic conditions, to either keep the
vehicle travels via a shorter path in an un-congested region or to explore
longer diverted paths in a congested region. We found that the optimal number
of steps to trigger an update of path-greediness is dependent on the density of
vehicles, and the magnitude of path-greediness increment affects the
macroscopic traffic conditions of the system. To better coordinate vehicles in
denser networks, the update on the tendency for vehicles to travel via the
shorter paths should be gradual and less frequent.
