Modeling crowd dynamics through coarse-grained data analysis
Sebastien Motsch1, Mehdi Moussaïd, Elsa G Guillot
1School of Mathematical and Statistical Sciences, Arizona State University, Tempe, USA.
Mathematical Biosciences and Engineering : MBE
|November 13, 2018
Summary
We developed a Bi-directional Macroscopic (BM) model to predict crowd movement. Laboratory experiments show the BM model accurately captures pedestrian flow, aiding crowd management and safety.
Area of Science:
- Physics
- Urban Planning
- Social Sciences
Background:
- Effective crowd management is crucial for urban safety and efficiency.
- Current systems lack rapid prediction capabilities for dynamic crowd behavior.
Purpose of the Study:
- To introduce a Bi-directional Macroscopic (BM) model for predicting crowd movement.
- To validate the model's accuracy using experimental data.
- To explore optimization strategies for crowd traffic efficiency.
Main Methods:
- Developed a Bi-directional Macroscopic (BM) model based on pedestrian flux and density.
- Conducted laboratory experiments with 119 participants in a circular corridor.
- Utilized experimental data to validate the BM model's predictive performance.
Main Results:
- The BM model accurately captured experimental data for bi-directional pedestrian flows.
- The model demonstrated effectiveness in a typical crowd forecasting scenario.
- A segregation strategy was proposed and analyzed for traffic enhancement.
Conclusions:
- The BM model provides a reliable core for crowd traffic management systems.
- It enables on-the-fly prediction of crowd movements for real-time optimization.
- The model supports the deployment of tailored crowd control strategies.
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