Abstract:

For the online optimization of pedestrian flow control in subway station, an algorithm frame for pedestrian flow control in subway station based on machine learning is designed. The pedestrian flow control process of a subway station during morning rush hour is selected,and the agent-based model is built to simulate the control process. The training data is collected through the multiple runs of the model, which is used as the input of deep reinforcement learning network, and the mature net is obtained through adequate training to provide the optimizing scheduling policy. Linking the actual data with the mature net to realize the real-time schedule optimization of subway pedestrian flow control. Simulation experiments show that the framework of the deep reinforcement learning can realize the on-line optimization and the performance is better than traditional algorithm.

Key words: deep reinforcement learning, pedestrian flow control, scheduling strategy in subway station, online simulation, real-time optimization