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Increased stress, fuel consumption, air pollution, accidents and delays aresome of the consequences of traffic congestion usually incurring in tremendouseconomic impacts, which society aims to remedy in order to leverage asustainable development. Recently, unconventional means for modeling andcontrolling such complex traffic systems relying on multi-agent systems havearisen. This paper contributes to the understanding of such complex and highlydynamic systems by proposing an open-source tool-chain to implementmulti-agent-based solutions in traffic and transportation. The proposedapproach relies on two very popular tools in both domains, with focus ontraffic light control. This tool-chain consists in combining JADE (Java AgentDEvelopment Framework), for the implementation of multi-agent systems, withSUMO (Simulation of Urban MObility), for the microscopic simulation of trafficinteractions. TraSMAPI (Traffic Simulation Manager Application ProgrammingInterface) is used to combine JADE and SUMO allowing communication betweenthem. A demonstration of the concept is presented to illustrate the mainfeatures of this tool-chain, using Q-Learning as the reinforcement learningmethod for each traffic light agent in a simulated network. Results demonstratethe feasibility of the proposed framework as a practical means to experimentwith different agent-based designs of intelligent transportation solutions.

JADE, TraSMAPI and SUMO: A tool-chain for simulating traffic light control