OD-network-based Pedestrian-path Prediction for People-flow Simulation

Simulating the movement of pedestrians is a challenging problem and becoming increasingly important in a variety of applications, such as determining potential safety hazards, evaluating operational performance, layout planning in public and commercial facilities. We propose a pedestrian-path prediction method for people-flow simulation, which takes into account the following two components: 1) dividing measured trajectory data into partial trajectories with each origin-destination pattern, and 2) training destination-wise models for pedestrian-path prediction with relative features based on velocities and distances We also discuss the path-prediction rate and people-flow simulation results with the proposed method using measured trajectory data in a check-in lobby at an airport.