A data-driven neural network approach to simulate pedestrian movement

As artificial intelligence becomes a research hotspot, more and more researchers are trying to apply it to numerous domains. It is therefore interesting and challenging to apply data-driven neural network technology to pedestrian movement modeling to test its effect against traditional social force model which can be applied to manifold pedestrian scenarios. Recent neural network based pedestrian movement simulation studies often train the network with only one scenario and then test within this scenario with various parameters. To make a more adaptive neural network, we propose a four layer network to learn multiple scenario data by normalization of relative positions among pedestrians, transferring velocity vector to scalar and incorporating more path planning information, and thus to make it more adaptive to various scenarios. Simulation results show that the proposed neural network approach can be applied to several typical pedestrian scenarios including counterflow and evacuation. Moreover, it shows more realistic speed-density curve and generates less trajectory fluctuations compared with social force model. Therefore, the proposed method is capable of generating more realistic pedestrian flow in multiple scenarios.