Autonomous vehicles are anticipated to revolutionize ride-sharing services and subsequently enhance the public transportation systems through a first-last mile transit service. Within this context, a fleet of autonomous vehicles can be modeled as a Dial-a-Ride-Problem with certain features. In this study, we propose a holistic solving approach for this problem, which combines the mixed-integer linear programming formulation with a novel graph dimension reduction method based on the graph embedding framework. This latter method is effective since accounting for heterogeneous travel demands of the covered territory tends to increase the size of the routing graph drastically, thus rendering exact solving of small instances computationally infeasible. An application is provided for the real transport demand of the industrial district of “Vallée de la Chimie” in Lyon city, France. Instances involving more than 50 transport requests, and 10 vehicles could be easily solved. Results suggest that this method generates routes of reduced nodes with a lower vehicle kilometers traveled compared to the constrained K-means based reduction. Reductions in terms of GHG emissions are estimated to be around 75% lesser than the private vehicle mode in our applied service. A sensitivity analysis is also provided.