The social implementation of small autonomous mobile robots is advancing in delivery and accompanying services. However, inappropriate waiting positions can obstruct pedestrian flow and impair facility operations. Conventional waiting location estimation methods require long-term observation of actual pedestrian walking history, resulting in high introduction costs and environmental dependence. This research proposes a fluid simulation-based method that estimates waiting locations using only floor maps without prior observation. By approximating pedestrian flow as a two-dimensional incompressible fluid, the method extracts waiting candidates from velocity fields determined by environmental geometry. Through comparative verification with agent-based simulation and human subject experiments, we confirmed that the proposed method achieves estimation accuracy equivalent to history-based approaches while requiring no prior observation. The method demonstrates environmental adaptability across simple corridors to complex spaces, with a practical threshold of 0.4 times maximum flow velocity effectively distinguishing appropriate waiting locations. The approach enables rapid deployment and adaptation to layout changes, addressing key challenges in mobile robot social implementation.