Abstract : In recent years, urban traffic congestion and air pollution have become huge problems in many cities in the world. In order to reduce congestion, we can invest in improving city infrastructures. Infrastructure improvements, however, are very costly to undertake and do not reduce air pollution. Hence we can work on intelligent mobility in order to have a more efficient car use. The application of new information technologies, such as multi-agent technologies to urban traffic information control, has made it possible to create and deploy more intelligent traffic management like DRT (Demand Responsive Transport) system. The objective of multi-agent based DRT system is to manage taxis in an intelligent way, to increase the efficient number of passengers in every vehicle, and at the same time to decrease the number of vehicles on streets. This will reduce the CO2 emissions and air pollution caused by the vehicles, as well as traffic congestion and financial costs. Multi-agent simulation has been looked as an efficient tool for urban dynamic traffic services. However, the main problem is how to build an agent-based model for it. This research presents a multi-agent based demand responsive transport (DRT) services model, which adopts a practical multi-agents planning approach for urban DRT services control that satisfies the main constraints: minimize total slack time, client's special requests, increases taxis' seats use ratio, and using minimum number of vehicle etc. In this thesis, we propose a multi-agent based multi-layer distributed hybrid planning model for the real-time problem. In the proposed method, an agent for each vehicle finds a set of routes by its local search, and selects a route by cooperation with other agents in its planning domain. By computational experiments, we examine the effectiveness of the proposed method. This research is supported by project "Gestion Temps Réel du Transport Collectif à la Demande" (CPER) Budgetthe French.