<p>Ride-hailing has become a mainstream way of urban transportation. Analyzing its macro-situation is important to discover people’s travel activities to mitigate the pressure in high-order areas. However, due to the high dynamics and spatiotemporal complexity of the data, it is difficult to understand the features and evolutionary trends of the situation. Besides, the traffic element does not include quantification methods for the situation. Currently, most research focuses on demand forecasting, ignoring the abundance of information in its macro-situation and lacking a complete situation awareness analysis process. To solve these problems, we propose a situation quantification method and predict situation values and situation correlations based on GraphSAGE. We also introduce RH-SAVis, a visual analytics system designed for ride-hailing situation awareness analysis, to explore its macro-situation and human travel activities at multiple time scales and levels for a complete SA analysis. We design an optimal layout algorithm for trajectory view to improve the spatial layout and innovative node-link views for situation spread analysis to understand the future trends of the situation. Meanwhile, our system provides users diverse views and flexible interactions to explore. Three case studies with experts participation and a user study validate the effectiveness of the system.</p> Graphical abstract <p></p>