Automated vehicles (AVs) receive tremendous attention and achieve rapid development. It is foreseeable that hybrid operations of human driving vehicles and automated vehicles in the urban transportation environment will be a long-standing state. To investigate the influence of AVs on the hybrid ride-hailing market, data-driven agent-based modeling and simulation (D²ABMS) for large-scale transportation networks is proposed, in which human drivers, automated vehicles, and passengers form three types of agents. D²ABMS goes beyond existing approaches by employing data-driven multi-objective deep learning to learn ride-sourcing drivers' offline/online behavior. E mbedding is used to represent the hidden attributes of different classes of drivers. Ride-sourcing data collected from the city of Hangzhou, China, are used to train and validate the drivers' decision-making model. Hybrid operations of human driving vehicles and automated vehicles with D²ABMS are comprehensively tested in various scenarios. The results show that a small proportion of automated vehicles in the hybrid ride-hailing market can significantly reduce the average waiting time of passengers. Besides, compared to the human driving scenario, the total exhaust emissions and vehicle kilometers traveled can be reduced by 12.3% in the AVs scenario. The proposed D²ABMS system has the potential to help transportation planners and ride-hailing platforms to assess their policies and operations management strategies in the era of shared mobility and automated vehicles.