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A data parallel approach to modelling and simulation of large crowd

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Abstract

The modeling and simulation (M&S) of large crowd has become increasingly important in the domain of public security, such as facility planning, disaster response, and anti-terrorism operations. The behavior of a large crowd is highly complex, and the M&S of a large crowd at the individual level therefore demands the support of a scalable and efficient computing technology. In this study, a method was proposed to formulate crowd behavior with the cell automata and multi-agent models, which were successfully mapped onto the MapReduce programming model. A simulation framework was developed upon Hadoop to simulate large crowd scenarios over a cluster. The simulation process was then transformed to a series of parallel operations on data streams. The simulation studies on a large-scale evacuation scenario had indicated that the simulation framework ensured the simulation process’ logic correctness. Experimental results also showed that the Hadoop-based simulation framework could complete five times more tasks while consuming only 19 % CPU time in comparison with the conventional simulation technology.

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Authors and Affiliations

  1. Institute for Network Science and Cyberspace, Tsinghua University, Beijing, 100084, China

    Tao Yu

  2. School of Computer Science, China University of Geosciences, Wuhan, 430074, China

    Minggang Dou & Mao Zhu

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  1. Tao Yu
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  2. Minggang Dou
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  3. Mao Zhu
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Correspondence to Minggang Dou.

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Yu, T., Dou, M. & Zhu, M. A data parallel approach to modelling and simulation of large crowd. Cluster Comput 18, 1307–1316 (2015). https://doi.org/10.1007/s10586-015-0451-y

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  • DOI: https://doi.org/10.1007/s10586-015-0451-y

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