Using Topological Statistics to Bias and Accelerate Route Choice: Preliminary Findings in Synthetic and Real-World Road Networks

This paper discusses the first steps towards the definition of novel statistics and metrics that characterize networks in terms of the complexity they pose to the traffic assignment problem. Here, we follow an approach in which the assignment emerges from routes selected by learning agents. Specifically, we deal with issues related to how routes are coupled. We first define and quantify route coupling, i.e., how much a given route is coupled with other routes that can be used by learning agents. The investigation of route coupling is important in multiagent reinforcement learning settings since it measures how a change in action selection by one agent interferes with the actions taken by other agents. Our preliminary empirical results indicate that using route coupling to bias the learning process of agents results in faster convergence in the traffic assignment problem.