A data parallel approach to modelling and simulation of large crowd
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.
mapreduce hadoop data parallel computing modeling and simulation of complex systems cloud computing