This paper proposes a macroscopic fluid modeling framework to assist with strategic decision making of a platform for operating a large-scale on-demand app-based ride-hailing system. The framework captures the spatiotemporal characteristics of a ride-hailing system, and is flexible in representing control policies that a platform is implementing. It thus enables the analysis of a large-scale ride-hailing system with observable market responses and facilitates the optimization of the control polices. As a demonstration of the proposed framework, we customize it for a ride-hailing system operated by Didi Chuxing in a large city of China, and conduct an empirical study of the system. In doing so, we calibrate the model with a high degree of granularity using empirical data, and then investigate optimal pricing strategies to achieve various managerial objectives of the platform. To our best knowledge, the proposed model is the first of its kind that offers a tractable way to support the analysis and optimization of large-scale ride-hailing systems. The empirical study also provides one of the first testbeds for examining various operations strategies for ride-hailing services.