The problem of traffic congestion incurs numerous social and economical repercussions and
has thus become a central issue in every major city in the world. For this work we look at the …

We consider the route choice problem using multiagent reinforcement learning. In this
problem, agents individually learn which routes minimise their expected travel costs. Such a …

Multi-agent systems (MAS) offer a powerful paradigm for modelling distributed settings that
require robust, scalable, and often decentralised control solutions. MAS applications vary …

Multiagent systems (MAS) offer a powerful paradigm for modelling distributed settings that
require robust, scalable, and often decentralised control solutions. Despite its numerous …

In transportation networks, users typically choose routes in a decentralized and self-
interested manner to minimize their individual travel costs, which, in practice, often results in …

In the micro-tolling paradigm, different toll values are assigned to different links within a
congestible traffic network. Self-interested agents then select minimal cost routes, where …

This paper aims to develop a paradigm that models the learning behavior of intelligent
agents (including but not limited to autonomous vehicles, connected and automated …

People have been working long to tackle the traffic congestion problem. Among the different
measures, traffic tolling has been recognized as an effective way to mitigate citywide …

The optimization of traffic flow on roads and highways of modern industrialized countries is
key to their economic growth and success. Besides, the reduction of traffic congestions and …

How to choose a route that takes you from A to B? This is an issue that is turning more and
more important in modern societies. One way to address this agenda is through the use of …