Dang, H.-T.; Gaudou, B.; Verstaevel, N. HyPedSim: A Multi-Level Crowd-Simulation Framework—Methodology, Calibration, and Validation. Sensors2024, 24, 1639.
Dang, H.-T.; Gaudou, B.; Verstaevel, N. HyPedSim: A Multi-Level Crowd-Simulation Framework—Methodology, Calibration, and Validation. Sensors 2024, 24, 1639.
Dang, H.-T.; Gaudou, B.; Verstaevel, N. HyPedSim: A Multi-Level Crowd-Simulation Framework—Methodology, Calibration, and Validation. Sensors2024, 24, 1639.
Dang, H.-T.; Gaudou, B.; Verstaevel, N. HyPedSim: A Multi-Level Crowd-Simulation Framework—Methodology, Calibration, and Validation. Sensors 2024, 24, 1639.
Abstract
Large-scale crowd phenomena are complex to model because the behaviour of pedestrians needs to be described at both strategic, tactical, and operational levels and is impacted by the density of the crowd. Microscopic models manage to mimic the dynamics at low densities, whereas mesoscopic models achieve better performances in dense situations. This paper proposes and evaluates a novel agent-based architecture to enable agents to dynamically change their operational model based on local density. The ability to combine microscopic and mesoscopic models for multi-scale simulation is studied through a use case of pedestrians at the Festival of Lights, Lyon, France. Pedestrian outflow data is extracted from video recordings of exiting crowds at the festival. The hybrid model is calibrated and validated using a genetic algorithm that optimizes the match between simulated and observed outflow data. Additionally, a local sensitivity analysis is then conducted to identify the most sensitive parameters in the model. Finally, the performance of the hybrid model is compared to different models in terms of density map and computation time. The results demonstrate that the hybrid model has the capacity to effectively simulate pedestrians across varied density scenarios while optimizing computational performance compared to other models.
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
