Even without a separate tracking algorithm, the directions of arrival (DOAs) of moving talkers can be estimated with a deep neural network (DNN) when the movement trajectories used for training allow the generalization to real signals. Previously, we proposed a framework for generating training data with time-variant source activity and sudden DOA changes. Slowly moving sources could be seen as a special case thereof, but were not explicitly modeled. In this paper, we extend this framework by using small jumps between neighboring discrete DOAs to simulate gradual movements. Further, we investigate the benefit of a latency controlled bidirectional recurrent layer in the DNN architecture, such that the required strictly limited context of future frames may still be acceptable for real-time applications. Experiments with real recordings show that the revised data generation leads to more continuous DOA paths, whereas the future context enables a quicker detection of speech onsets and offsets.