Development of Pedestrian Crowd Simulation on
GPU and Application to Evacuation from
Large-scale Underground Shopping Mall
Cybermedia Center, Osaka University, Japan
CASE STUDY AND PERFORMANCE

Pedestrian crowd simulations have been developed and applied toverification of the safety in buildings or urban environments. Agentbased simulation are particularly well suited to pedestrian crowd
behaviors from a set of simple individual rules. However,computing and visualizing crowd behaviors in real-time is acomputationally intensive task because this intensity mostly comesfrom the O (n2) complexity of the algorithm needed for theinteractions of all agents. By using special data structures such asgrids, relevant previous works reduces this complexity.
On the other hand, researchers demonstrated significantly
increased speed-up after adapting existing CPU-oriented
algorithms to parallel processing. Furthermore, modern GPUs have
many cores; they offer large performance benefits for parallel
processing at low cost. In 2007, NVIDIA released CUDA
parallel-processing architecture for next-generation GPUs, letting
programmers use C. It is becoming easier to develop agent based
simulation on GPU.
In this study, we present a GPU based implementation ofagent-based pedestrian crowd simulation and an application to theevacuation from a large-scale underground shopping mall.

As a case study of the pedestrian crowd simulation, we configured
an evacuation situation in a large-scale underground shopping mallin Osaka, Japan in Figure 3.
As a performance testing, we evaluated two versions of thesimulation. While the first was executed on the GPU, the second
was executed on the CPU. Both versions used the same algorithmand data structure. The average time to compute the social force
of all agents without rendering process is shown in Figure 4. Thesesimulations were executed for each implementation type varying
only the number of agents, ranging from 1,000 to 10,000. As aresult, the GPU version presents better scalability than the CPUversion. When the number of agent is 10,000, the GPU version isapproximately seven times faster than the CPU version. Moreover,
it is possible to see that the performance of the GPU
implementation sustained interactive frame rates with rendering ofcomplex models of agents and buildings.

IMPLEMENTATION
Agent-Based Modeling
We implemented the social force model in order to model
pedestrian crowd behavior. The social force model solves themotion equation of an agent, which is represented by a moving
disc (Figure 1). An agent that has mass and a constant radius issubjected to a force exerted by other agents and obstacles andexit position. These steps perform all operations on a GPU using
CUDA (Figure 2). Parallel processing and the GPU’ s many coresproduce huge computational power, enough to update massive
pedestrian crowds.

Fig3. Application to Large-scale Underground Shopping MallFig.1 Social Force Model

These tests were performed on an Intel Core i7 CPU 930@2.80GHz with a NVIDIA GeForce GTX 460 GPU

Fig. 4 Performance Result of CPU and GPUConclusion
We showed an implementation capable of running up to 10,000
abents at an interactive frame rate using current graphics
hardware and CUDA technology
Fig2. Operations on GPU

Contact: Kensuke Yasufuku, Ph.D.
E-mail: yasufuku@cmc.osaka-u.ac.jp

Development of Pedestrian Crowd Simulation on GPU and Application to Evacuation from Large-scale Underground Shopping MallINTRODUCTION