  Increased stress, fuel consumption, air pollution, accidents and delays are
some of the consequences of traffic congestion usually incurring in tremendous
economic impacts, which society aims to remedy in order to leverage a
sustainable development. Recently, unconventional means for modeling and
controlling such complex traffic systems relying on multi-agent systems have
arisen. This paper contributes to the understanding of such complex and highly
dynamic systems by proposing an open-source tool-chain to implement
multi-agent-based solutions in traffic and transportation. The proposed
approach relies on two very popular tools in both domains, with focus on
traffic light control. This tool-chain consists in combining JADE (Java Agent
DEvelopment Framework), for the implementation of multi-agent systems, with
SUMO (Simulation of Urban MObility), for the microscopic simulation of traffic
interactions. TraSMAPI (Traffic Simulation Manager Application Programming
Interface) is used to combine JADE and SUMO allowing communication between
them. A demonstration of the concept is presented to illustrate the main
features of this tool-chain, using Q-Learning as the reinforcement learning
method for each traffic light agent in a simulated network. Results demonstrate
the feasibility of the proposed framework as a practical means to experiment
with different agent-based designs of intelligent transportation solutions.
