With the tide of electrifying urban transportation systems by introducing electric vehicles, the differences between fuel vehicles and electric vehicles in driving styles and strategies to achieve eco-driving have become a burden for efficient operations of urban transportation systems. Most of the previous energy management strategies have sought to achieve system optimisation at a single-vehicle or multi-vehicles level, and failed to consider the vehicle-to-vehicle and vehicle-to-infrastructure effects in a global optimisation manner. Furthermore, as a typical human-in-the-loop cyber–physical system, the mobility behaviours of road users undoubtedly play a vital role in the cooperative and green operations of urban transportation systems. Yet little research has dedicated to develop means to incentivise energy-saving behaviours in transportation systems. Hence, in this paper, we propose a unifying queueing and neural network model to calculate the time and energy efficient course of actions and routes for different types of road users within an urban road network in a real time manner. The lower-level queueing model captures the interactive dynamics of road users and solves the optimal flow ratio at each intersection while the upper-level neural network model further customises desired routes for different types of road users. In addition, an incentive mechanism is proposed to encourage road users to follow the optimal actions via publishing various types of reward-gaining tasks. A case study in a designated area of Beijing shows that the use of the bi-level optimisation algorithm can reduce the average travel time by approximately 20% and decrease the energy consumption by 10% in comparison with the realistic trip data.