This extended abstract outlines the benefits of implementing citizen-centric design principles into a demand-responsive transportation optimization system. Demand-responsive transportation systems work on flexible schedules to predict and react to user demand in real-time. Additionally, areas where social preferences can be incorporated into these methods, are identified. Then a comparison between a Tabu search heuristic and a simple greedy heuristic for passenger stop selection is outlined. An ant colony heuristic handled the primary vehicle routing. The results of these tests indicate a benefit to the users of the transportation system when these design principles are implemented. However, more work is required to add essential features, such as dynamic elements, to the model as well as improve the overall efficiency of the method. Finally, a path to these improvements as well as potential extensions to work is discussed, including focus groups, wider surveys and further experiments.