Crowd disasters as systemic failures: analysis of the Love Parade disaster

By Dirk Helbing, Pratik Mukerji. EPJ Data Science, 2012

391 citations recorded by the source corpus.

Each year, crowd disasters happen in different areas of the world. How and why do such disasters happen? Are the fatalities caused by relentless behavior of people or a psychological state of panic that makes the crowd ‘go mad’? Or are they a tragic consequence of a breakdown of coordination? These and other questions are addressed, based on a qualitative analysis of publicly available videos and materials, which document the planning and organization of the Love Parade in Duisburg, Germany, and the crowd disaster on July 24, 2010. Our analysis reveals a number of misunderstandings that have widely spread. We also provide a new perspective on concepts such as ‘intentional pushing’, ‘mass panic’, ‘stampede’, and ‘crowd crushes’. The focus of our analysis is on the contributing causal factors and their mutual interdependencies, not on legal issues or the judgment of personal or institutional responsibilities. Video recordings show that people stumbled and piled up due to a ‘domino effect’, resulting from a phenomenon called ‘crowd turbulence’ or ‘crowd quake’. Crowd quakes are a typical reason for crowd disasters, to be distinguished from crowd disasters resulting from ‘mass panic’ or ‘crowd crushes’. In Duisburg, crowd turbulence was the consequence of amplifying feedback and cascading effects, which are typical for systemic instabilities. Accordingly, things can go terribly wrong in spite of no bad intentions from anyone. Comparing the incident in Duisburg with others, we give recommendations to help prevent future crowd disasters. In particular, we introduce a new scale to assess the criticality of conditions in the crowd. This may allow preventative measures to be taken earlier on. Furthermore, we discuss the merits and limitations of citizen science for public investigation, considering that today, almost every event is recorded and reflected in the World Wide Web.

Subfields: Computer Vision and Pattern Recognition, Ocean Engineering, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/83e133b4244f8d7d73259aea2ddbae8b044e6177

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

In Ocean Engineering

In Statistical and Nonlinear Physics

Citations in the corpus

Saving Human Lives: What Complexity Science and Information Systems can Contribute (2014, neutral): “Even though this is a well-established fact in academic circles, it remains a frequent source of crowd disasters [26].”
Composition Loss for Counting, Density Map Estimation and Localization in Dense Crowds (2018, background): “Although they generally have passive crowds coming together for peaceful purposes, disasters have known to occur, for instance, during Love Parade [9] and Hajj [1].”