Under the background of city-station combination, the different interaction between “fast going to work” and “slow staying” passenger streams inside big city railway hubs causes serious time-space conflicts; The traditional passive, static control approaches have difficulty in dealing with the impulse surges which are generated by incoming trains. For the solving of the zero-sum game problem that lies between traffic evacuation and commercial attraction, this thesis puts forward a dynamic collaboration optimization method for passenger flow on the basis of Model Predictive Control (MPC). Firstly, the complicated space arrangement of the hub is by us abstracted to be a direction queuing network. An innovative multidimensional Macroscopic Fundamental Diagram (MFD) evolution model—incorporating the proportions of heterogeneous passenger flows—is then constructed to precisely quantify the nonlinear frictional impedance that commercial lingering behaviors impose on main-line traffic flow. Second, a multivariable feedforward MPC architecture is established to implement proactive flow restrictions and dynamic path guidance by simulating the trajectories of congestion shockwaves through a rolling prediction mechanism. Simulation results, based on the Chongqing Shapingba Hub as a case study, demonstrate that this system can reduce the maximum queue length at core ticket gates by 58.1% and narrow the variance in travel delays by 67.6%. Furthermore, by identifying a balanced solution on the Pareto frontier—trading a marginal increase in travel delay for a substantial boost in commercial attraction—the system achieves a deeply adaptive collaboration between anti-stampede safety protocols and the economic efficiency of the urban micro-center.
