In recent years, mobility on demand has experienced a major revival due to various ride-hailing companies entering the market. Competing in this field requires an efficient operation. Therefore, the applied policy, which cares for vehicle-to-customer assignment and vehicle repositioning, has to achieve good customer service and minimize cost while trying to keep the impact on the environment as low as possible. A promising approach is to coordinate the control of the entire fleet, which is foreseen to become even easier with the possibility of autonomous vehicles in mind. Anticipating future demand requires a good understanding of the spatiotemporal distributions of request origins and destinations, and the resulting imbalance between vehicle demand and availability. This results from a multitude of topological, demographic, and social effects, which are almost impossible to sufficiently capture in a handcrafted model of reasonable complexity. This can be circumvented by leveraging machine learning approaches. In this paper, an image-like representation of the city and its fleet's state is introduced. It is comprehensive and intuitive to use as input to convolutional neural networks, which in the past have already been proven to capture spatial relationships very well. This allows operating on realistic, full-sized traffic networks without greatly increasing the number of parameters the neural network has to learn and, hence, keeps the training effort low. Additionally, this state is combined with a similarly constructed repositioning action, reflecting a 2D distribution of a well-performing operational policy. This approach allows replacement of complex, handcrafted mathematical models by a single, compact, auto-encoder-like neural network.