Computational decision support for crowd management applications

A case study on operational in-event pedestrian crowd management

Master thesis (2022)

Authors

F.T. Boendermaker Technology, Policy and Management

Contributors

C.N. van der Wal System Engineering - TPM (supervisor 1)
Willem L. Auping Policy Analysis - TPM (supervisor 2)
D.C. Duives Transport and Planning - CEG (supervisor 2)
C. Kuster Geodan (supervisor 2)
Faculty
Technology, Policy and Management
Copyright: © 2022 Floris Boendermaker
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Published Date

07-07-2022

Language

English

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Faculty

Technology, Policy and Management

Abstract

Crowd management is a crucial element in keeping situations safe. Models can help understand in-event crowd management system more thoroughly, and illustrate potential effects of measures before they have to be implemented in real life. However, applications of crowd models for operational support on in-event crowd management are sparse. Two main reasons are the cause of this: (1) inherent uncertainty regarding crowd modelling; and (2) computational requirements regarding large-scale applications. This research proposed three methodological steps for model-based experimentation—exploration, selection, and evaluation—to overcome current challenges in the application of crowd models. These steps were then applied on a case study of operational in-event crowd management at the Grote Markt, in the city of Breda. The research question thereby was: “What effect do the in-event crowd management measures—traffic regulators, directional guidance, and object placement—have on the density and walking speed of pedestrians in the Grote Markt, Breda?”. To answers this question, this study: (1) constructed a detailed microscopic crowd model of the Grote Markt, utilizing open-source crowd simulation framework Vadere for rapid, yet sophisticated, development of an agent-based model; and (2) applied this model according to the proposed steps for model-based experimentation, using techniques from the field of exploratory modelling and analysis. A connector between Vadere and the Python based Exploratory Modelling and Analysis (EMA) Workbench was constructed to synergize these two steps. Main findings highlight the potential of the proposed traffic regulator measure, and its effectiveness compared to object placement and directional guidance. With the proposal of the methodological steps, this work provides the needed stepping stone for operational support on crowd management. One that utilizes crowd models to understand in-event crowd management systems, and thereby enables the comparison between different in-event measures before they have to be implemented in real life.