text: 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.
  @cite_19: This paper consolidates existing research and provides a first step in the establishment of a comprehensive, multi-agent system taxonomy. This taxonomy addresses characteristics of the overall agent environment, the agent population, and the characteristics of the agents in the population. Reasoning, belief, and cooperative mechanisms are all addressed as are the environmental characteristics in which the agents are situated. Areas for additional taxonomy development work are identified along with possible applications of the taxonomy in future research.
  @cite_20: One way to cope with the increasing demand in transportation networks is to integrate standard solutions with more intelligent measures. This problem has been approached from different sides such as the study of the assignment of the demand in the network, and the investigation of the effects of control measures. However, given that most of these approaches are complex and deal with different levels of abstraction of the original problem, there has been few attempts to address both demand and control in a single tool. This paper presents an overview of ITSUMO, a microscopic traffic simulator whose implementation uses agent technologies with a bottom-up philosophy in mind. We give an overview of the system and some details of its modules (simulation kernel, data management, control, driver and routing, and visualization), followed by an example of its use. I. I NTRODUCTION AND MOTIVATION
summary: To date, there are not many solutions for traffic that make full use of the intelligent agent concept. However, the multi-agent system approach has become recognized as a convenient approach for modelling and simulating complex systems @cite_19 . Also, it has grown enormously not only applied to traffic but also to transportation in general terms @cite_20 .