A Micro-Meso-Macro Approach to Intelligent Transportation Systems

Despite all the technological advances in the automotive industry and the high instrumentation of road infrastructures, driving a vehicle is still a stressful and polluting operation. In order to achieve a more sustainable use of vehicles, in this paper we propose a three-layered (micro-meso-macro) framework that provides intelligence at different levels of detail of the driving experience. At the micro level, intelligence is provided to the individual vehicle, at the meso level, intelligence is concerned with local clusters of vehicles and group decision making, and at the macro level, intelligence is provided so that system-wide goals are achieved. We illustrate each of these levels with a particular implementation, although alternative implementations may be used as well. Namely, we use an affective anticipatory architecture for the micro level, a consensus algorithm at the meso level, and a resource allocation mechanism together with electronic institutions for the macro level. We believe that the combination of intelligences at each of the levels may lead to a more efficient and sustainable use of the vehicles and the infrastructure.