In an attempt to address the transportation problems now ubiquitous, may them be financial, environmental or any, we are mainly involved with the establishment of a dynamic optimized carpooling service. Shared cars came to remedy these problems and meet the longtime remained unsatisfied needs (spatiotemporal flexibility…) and so promote the comodal practice. The stress is then put on the complementarity between collective and individual means of transportation and comes to confirm the shared car and more particularly the carpooling as a transport mode as a whole. Based on this, we are mainly interested in setting up a real time ridesharing service providing the needed efficiency in such a context. In fact, the problem we tackle has a complexity of exponential order which must be wiped out preventing from adverse impacts. Blending the agent paradigm with the optimization technics helped reach our goals of implementing a large-scale competitive and fully automated support and providing the necessary efficiency and quality of service. The proposed alliance is realized through communicating optimizing agents spread according to a distributed dynamic graph modeling. The latter is established through a subdivision process of the served geographic network and has been inspired from clustering technics to put the stress on limited and intersecting areas of high density. This helps to promote the parallel requests treatment over a decentralized process. Thus, each optimizing agent firstly manage the requests parts included within the zone it is responsible for and then recompose global responses in coalition with concerned agents in a distributed artificial intelligence context
