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The work scheduling flexibility enjoyed by drivers of ride-sourcing platforms poses challenges for these platforms in effectively managing their workforce. A key to resolving these challenges is to understand how drivers set up their work schedule so as to inform management decisions. This letter summarizes our investigation of ride-sourcing drivers' working hour decisions. Leveraging comprehensive data from Didi Chuxing, a ride-hailing company in China, we calibrate two classic models from previous studies on taxi labor markets to examine drivers' reactions to earnings in deciding their working hours. Our results suggest that, when deciding whether to continue a shift, drivers pay more attention to future income opportunities and the time already spent working rather than their current earning level. [ABSTRACT FROM AUTHOR]

• A macroscopic fluid modeling framework for on-demand ride-hailing system. • Strategic decision making of a platform for operating a large-scale ride-hailing system. • An empirical study is presented to demonstrate the framework. 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. [ABSTRACT FROM AUTHOR]