Utilizing Crowd Collectiveness to Enhance Bottleneck Detection Based on the Lagrangian Framework

At large events and other crowded areas narrowed passages create a major safety risk for the involved people. We present a novel approach to automatically detect the formation of bottleneck situations and, thus, aid decision - makers to mitigate potential safety threats. In our work we analyse the dynamics of motion by using the Lagrangian approach, known from the analysis of dynamic systems, to understand movements of groups of people. Characteristic congestion patterns can be recognised in the complex time - dependent movement dynamics with the help of Lagrangian measures. For this purpose, crowd collectiveness is introduced as a new measure, which can recognise random-looking movements of single individuals as group movements. With the new measure, an accuracy gain of 5% can be achieved for the detection of bottleneck situations compared to state-of-the-art approaches.