The rise of e-hailing taxis have significantly altered urban transportation and resulted in an competitive taxi market with both traditional street-hailing and e-hailing taxis. The new mobility services provide similar door-to-door rides as the traditional one and there is competition across these various services. Meanwhile, the increasing e-hailing supply, together with traditional taxicab flows, influence the urban road network performance, which can also in turn affect taxi mode choice and operation. In this study, we propose an innovative modeling structure for the competitive taxi market and capture the interactions not only within the taxi market but also between the taxi market and urban road system.The model is built on a network consisting of two types of queueing theoretic approaches for both the taxi and urban road system. Considering both the passenger and vehicle arrivals, we utilize an assembly-like queue SM/M/1 for passenger-vehicle matching within the taxi system, which controls how many and how frequently vehicles drive from the taxi system to the urban road system. A common multi-server M/M/c queue that can account for road capacity is proposed for the urban road system and a feedback of network states are sent back to the taxi system. Moreover, within the taxi system, we introduce state-dependent service rate to account for the stochasticity of passenger-vehicle matching efficiency. Then the stationary state distributions, as well as asymptotic properties, of the queueing network are discussed.An example is designed based on data from New York City. Numerical results show that the proposed modeling structure, together with the corresponding approximation method, can capture dynamics within high demand areas using multiple data sources. Overall, this study shows how the queueing network approach can measure both the taxi and urban road system performance at an aggregate level. The model can be used to estimate not only the waiting/searching time during passenger-vehicle matching but also the delays in the urban road network. Furthermore, the model can be generalized to study the control and management of taxi markets.