Dynamics of a Simulated Demonstration March: An Efficient Sensitivity Analysis

Abstract

Protest demonstrations are a manifestation of fundamental rights. Authorities are responsible for guiding protesters safely along predefined routes, typically set in an urban built environment. Microscopic crowd simulations support decision-makers in finding sustainable crowd management strategies. Planning routes usually requires knowledge about the length of the demonstration march. This case study quantifies the impact of two uncertain parameters, the number of protesters and the standard deviation of their free-flow speeds, on the length of a protest march through Kaiserslautern, Germany. Over 1000 participants walking through more than 100,000 m2 lead to a computationally demanding model that cannot be analyzed with a standard Monte Carlo ansatz. We select and apply analysis methods that are efficient for large topographies. This combination constitutes the main novelty of this paper: We compute Sobol’ indices with two different methods, based on polynomial chaos expansions, for a down-scaled version of the original set-up and compare them to Monte Carlo computations. We employ the more accurate of the approaches for the full-scale scenario. The global sensitivity analysis reveals a shift in the governing parameter from the number of protesters to the standard deviation of their free-flow speeds over time, stressing the benefits of a time-dependent analysis. We discuss typical actions, for example floats that reduce the variation of the free-flow speed, and their effectiveness in view of the findings.

Global Sensitivity Analysis Based on Entropy: From Differential Entropy to Alternative Measures

“…Firm conclusions on the selection of the best SA method could not be reached in the majority of the studies pertaining to SA, which is understandable, because there are numerous ways to define which is the best one [46]. Sobol SA is very popular (see, e.g., [47][48][49][50][51][52][53][54]); on the contrary, less unified types of SA are less widespread [55][56][57][58][59][60][61][62][63][64]. In general, a global SA, which analyzes the influence of the variability of inputs throughout their distribution range and can describe the influence of interactions between input variables on the output, can be recommended.…”