Nowadays, the protection of the environment through the reduction of greenhouse gases is becoming more and more important. In order to resolve environmental problems, a multimodal policy is firstly adopted in order to encourage the use of public transport. Since 2006, a new notion: the co-modality is introduced and it consists on developing infrastructures and taking measures and actions that will ensure optimum combination of individual and public transport modes. In this context, the purpose of this thesis is to implement co-modal transport system that covers all the existing transport services such as the public transport, the carpooling or the free use vehicles (bikes, cars). In order to satisfy the user’s requests, the system offers optimized co-modal itineraries in terms of three criteria: total time, total cost and greenhouse gases emission taking into account their preferences and constraints. In a short time interval, many transport users can formulate simultaneously a set of requests. So the system should find feasible decompositions in terms of independent sub-itineraries called Routes recognizing similarities and recognize the different possibilities of Routes Combinations to compose each itinerary demand. Considering the dynamic and distributed aspect of the problem, an effective strategy combining different concepts like multi-agent system and optimization methods is applied. The experimental results presented in this thesis justify the importance of co-modality and the necessity of taking advantage of the complementarity between the shared vehicles and other means of transportation through an intelligent and global system
