Machine-learning-based People-flow Simulation for Facility Layout Planning

We present a practical people-flow simulation method for evaluating facility layout plans; and demonstrate its effectiveness in experiments using real people-flow data. To improve the robustness of our previous machine-learning-based method, we use a single model trained from entire trajectory data, considering the negative effect of dividing up the training data. We also introduce feature values that represent positional relations between a pedestrian's destination and obstacles. To evaluate the performance of our method, people-flow data were obtained by using LiDAR sensors in an office before and after its layout was changed. In the experiments, we trained the prediction model from only the data before the layout change and found that the people-flow simulation was accurate even after the layout change. We also confirmed that our method outperformed the existing ones. As a result, it was confirmed that our people-flow simulation method can be used to evaluate facility layout plans.