The dynamic signal control and route choice equilibrium are usually integrated into a noncooperative game between the network authority and the road users. There are mainly two problems in most existing optimization methods. Firstly, the authority is often placed in the upper level in bi-level programming models, such a pure system-optimization-oriented framework may increase the difficulty in obtaining an equilibrium flow distribution. Secondly, the impact of drivers’ compliance rate on the control strategy has not been fully investigated, which makes the problem intractable in real time, specially in a connected vehicle (CV) environment. This paper proposes a modified Stackelberg Games Model to change the format of authority-user and user-authority dynamically. The direct communication between authority and users is established, the drivers’ compliance rate is applied as the level-change threshold index. Considering the difference between drivers’ realized travel time and the predicted travel time on Variable Message Sign (VMS), a logit model is formed to calibrate the compliance rate in every time step. Based on a modified Wavelet Neural Network algorithm, the model predictive control (MPC) fulfills the level-change procedure through the software Matlab 2018b. An Multi-Agent-System (MAS) based urban mixed network scenario is established, including several sub-agents like central agent, vehicle agent, intersection agent, signal agent, etc. Six benchmarks are applied in a numerical example. The results show that the proposed model with the centralized framework obtains the minimum total travel cost compared with benchmarks. Combined with the real-time mutual feedback between drivers’ response and control strategy, the level-change procedure potentially maintains the compliance rate within a certain level.

CICTP