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- W4385300745 abstract "Despite the close correlation between psychological states and behavior during civil aircraft evacuations, accurately characterizing the diverse reactions of passengers in cramped conditions remains a formidable task. The trajectory data for the evacuation of crowds in a confined cabin was obtained by modeling the seat row spacing, aisle width, and exit width of a Boeing 737. Furthermore, emergency evacuation experiments were conducted to investigate crowd behaviors under competitive and cooperative conditions. A novel neural network architecture, namely the Separated Multiple Convolution and Long Short-Term Memory Neural Network (SMCNN-LSTM), has been developed. The network incorporates an instantaneous behavior feature module, which enables the identification of passenger evacuation states through multiple convolutions. To capture the temporal features, we have adopted the continuous behavior feature module. This has been followed by utilizing the Softmax activation function for classification to recognize the evacuation behavior patterns of different groups. Our approach aims at capturing the intricacies of time-related aspects, enabling us to gain a deeper understanding of evacuation behavior. The results revealed that with identical aisle widths, the evacuation crowd behavior pattern can be accurately identified with a precision exceeding 95%. In cases with differing aisle widths, the accuracy of pattern recognition based on the original sample ranged from 60% to 85%, while recognition accuracy following symmetric flip enlargement of the sample surpassed 95%. In the meantime, a comparison of the five categorization algorithms reveals that the best precision is achieved. Furthermore, the discrete SMCNN-LSTM network demonstrates its efficacy in accurately identifying the diverse evacuation behaviors that arise from the distinct psychological conditions of passengers within the constrained environment of an aircraft cabin. Moreover, this approach can offer a verification technique relying on the identification behavior process to prognosticate evacuations, besides serving as a foundation for assessing the comparability and genuineness of simulated and observed behavioral patterns in populations." @default.
- W4385300745 created "2023-07-28" @default.
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- W4385300745 date "2023-09-01" @default.
- W4385300745 modified "2023-10-01" @default.
- W4385300745 title "Emergency evacuation behavior characteristics classification of aircraft cabin passengers based on deep learning network model SMCNN-LSTM" @default.
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- W4385300745 doi "https://doi.org/10.1016/j.physa.2023.129097" @default.
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