Parallel implementation of aircraft disembarking and emergency evacuation based on cellular automata

Abstract

In this paper we present a model based on the parallel computational tool of cellular automata (CA) capable of simulating the process of disembarking in a small airplane seat layout, corresponding to Airbus A320/ Boeing 737 layout, in search of ways to make it faster and safer under normal evacuation conditions, as well as emergency scenarios. The proposed model is highly customizable, with the number of exits, the walking speed of passengers, depending on their sex, age and height, and the effects of retrieving and carrying luggage. Additionally, the presence of obstacles in the aisles as well as the emergence of panic being parameters whose values can be varied in order to enlighten the disembarking and emergency evacuation processes are considered in detail. The simulation results were compared to existing aircraft disembarking and evacuation times and indicate the efficacy of the proposed model in investigating and revealing passenger attributes during these processes in all the examined cases. Moreover, we parallelized our code in order to run on a graphics processing unit (GPU) using the CUDA programming language, speeding up the simulation process. Finally, in order to present a fully dynamical anticipative real-time system helpful for decision-making we implemented the proposed CA model in a field programmable gate array (FPGA) device, and recreated the results given by the software simulations in a fraction of the time. We then compared and exported the performance results among a sequential software implementation, the implementation running on a GPU, and a hardware implementation, proving the consequent acceleration that results from the parallel CA implementation in specific hardware.

Increasing the Simulation Performance of Large-Scale Evacuations Using Parallel Computing Techniques Based on Domain Decomposition

“…Although GPGPU and FPGA coding looks attractive for evacuation simulation [32], due to their impressive performance compared to CPUs, it is difficult to apply to a rule based evacuation model where each agent will follow its own branching path of execution within the software making it unsuitable, at present, Increasing the Simulation Performance of Large-Scale Evacuationsfor a SIMD type processor [31] which can only perform a single execution branch. Another issue is the complexity of the buildingEXODUS software which has hundreds of thousands lines of code making writing a GPGPU or FPGA version a major undertaking even if it was suitable.…”

“…Using this strategy, many computers will potentially be responsible for handling the movement of a particular agent through the environment. This was the strategy adopted in other parallel implementations [30,[32][33][34][35].…”

“…Furthermore, most of the earlier parallel implementations were simply concerned with reducing the execution time of the simulation [30,33,34] but not necessarily as part of a live incident, where significantly faster than real time performance is required. Two implementations were specifically concerned with running faster than real time for use in a live incident [32,35]. The Giitsidis et al [32] model ran on specialist hardware (FPGA) is reported to achieve run times over 10 7 times faster than real time when applied to a small aircraft carrying 150 passengers.…”

“…Two implementations were specifically concerned with running faster than real time for use in a live incident [32,35]. The Giitsidis et al [32] model ran on specialist hardware (FPGA) is reported to achieve run times over 10 7 times faster than real time when applied to a small aircraft carrying 150 passengers. It is unclear, from the available literature, if this model could be extended to simulating larger environments.…”

“…There are three commonly used approaches to representing agent behaviour within microscopic evacuation models: Cellular Automata (CA) [24], Social Forces based models [25][26][27], and rule based models [9-12, 28, 29]. CA methods have been parallelised across multiple processors [30] but CA methods are particularly well suited to parallelisation on a SIMD (Single Instruction Multiple Data) platform [31] such as a General Purpose Graphical Processing Unit (GPGPU) [30,32,33] or a Field Programmable Gate Array (FPGA) [32] due to each grid cell being computed in the same fashion but with different data. However if complex interactions/behaviours are to be represented involving agents utilising information obtained from beyond their immediate vicinity the effectiveness of SIMD parallelism will be affected.…”