A data-driven path planning model for crowd capacity analysis

Abstract

In this paper, an agent-based crowd simulation model that focuses on path planning layer of (1) origin/destination popularities and (2) route choice is developed. This path planning model improves on the existing mathematical modeling and pattern recognition approaches by utilizing different sources of data to drive and validate it: video data was used for the open space scenarios and virtual reality experiments were applied for constrained space scenarios. For open space scenarios with video coverage, the density map of the video is extracted to calibrate the origin/destination popularities and the route probabilities among them. Factors related to space syntax, such as the traveling distance and turning angle, are proven effective features of the path planning model in this scenario. For constrained space scenarios, where the coverage of videos is usually limited, virtual reality experiments can be applied to learn the route choice model parameters at a fine granularity, particularly considering the crowdedness of the surroundings besides the space syntax factors. The navigation behaviors of players under different configurations in the virtual reality experiments were retrieved to train the route choice models using Support Vector Machine (SVM) model. The trained route choice model then simulates the crowd motion more realistically under different densities. We demonstrate the usefulness of the data-driven path planning model for crowd capacity analysis of a building layout.