Measurement of congestion and intrinsic risk in pedestrian crowds

Abstract

In this study, we present a method to quantify the amount of congestion and the intrinsic risk in pedestrian crowds. Levels of congestion are estimated based on the velocity vector field obtained from the analysis of video recordings of moving crowds. By using data collected during supervised experiments, we show that the so-called “congestion level” allows to define a threshold for congestion under safe conditions and to measure the smoothness of pedestrian flows. The proposed approach has been compared with alternative quantities such as density, flow or the “crowd pressure” showing a more universal and consistent description of crowd motion. Later, the “crowd danger” of different pedestrian streams has been computed confirming that multidirectional motion is more dangerous than unidirectional one for equal levels of density. From a more practical perspective, the congestion level allowed to get a complete picture of the region in front of bottlenecks and to identify the formation of organized structures also under constant density and flow conditions. In addition, since only velocities are used in the computational process of the congestion level, it is more suitable for applications involving computer vision and emerging technologies, since density is usually difficult to obtain in very crowded situations. The congestion level and the crowd danger may help in the design of pedestrian facilities by simplifying interpretation of results from simulation and efficiently identify hotspots or design flaws. Finally, crowd control may benefit from the methods presented by potentially allowing a clear identification of dangerous locations during mass events.