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In urban areas, the cost of road congestion has paid great attention to the sociological, technological and environmental aspects, such as the optimal route and fuel consumption. This step is towards a smarter vehicle mobility where the travel time will be planned and dynamically adapted to changes with actual status of the traffic flow. In this article a multi-objective ACO algorithm is proposed to solve the daily carpooling problem. In particular, a set of decision variables are proposed in order to minimize three objective functions subject to a set of constraints on these objectives.