Evacuation trajectory prediction of passengers in transport aircraft based on social-implicit model

CHEN Kun; LI Fang; FENG Zhen-yu; CHEN Xiang-ming; DUAN Long-kun

doi:10.19818/j.cnki.1671-1637.2024.05.018

Volume 24 Issue 5

Oct. 2024

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CHEN Kun, LI Fang, FENG Zhen-yu, CHEN Xiang-ming, DUAN Long-kun. Evacuation trajectory prediction of passengers in transport aircraft based on social-implicit model[J]. Journal of Traffic and Transportation Engineering, 2024, 24(5): 270-284. doi: 10.19818/j.cnki.1671-1637.2024.05.018

Citation:

CHEN Kun, LI Fang, FENG Zhen-yu, CHEN Xiang-ming, DUAN Long-kun. Evacuation trajectory prediction of passengers in transport aircraft based on social-implicit model[J]. Journal of Traffic and Transportation Engineering, 2024, 24(5): 270-284. doi: 10.19818/j.cnki.1671-1637.2024.05.018

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Evacuation trajectory prediction of passengers in transport aircraft based on social-implicit model

doi: 10.19818/j.cnki.1671-1637.2024.05.018

1.
Science and Technology Innovation Research Institute, Civil Aviation University of China, Tianjin 300300, China
2.
College of Aeronautical Engineering, Civil Aviation University of China, Tianjin 300300, China
3.
Aircraft Strength Research Institute of China, Xi'an 710065, Shaanxi, China
4.
College of Safety Science and Engineering, Civil Aviation University of China, Tianjin 300300, China

Funds:

National Key Research and Development Program of China 2022YFB4301000

Fundamental Research Funds for the Central Universities 3122017085

More Information

Author Bio:
CHEN Kun(1984-), male, associate professor, PhD, E-mail: kchen@cauc.edu.cn

FENG Zhen-yu(1966-), male, professor, PhD, E-mail: mhfzy@163.com
Received Date: 2024-04-20
Available Online: 2024-12-20
Publish Date: 2024-10-25

Abstract

Abstract

To accurately predict passenger evacuation trajectories in narrow spaces during emergencies of transport aircraft, a social-implicit model based on deep learning was developed. The model included three modules: social zone, social cell, and social loss. Passenger behavior changes and dynamic interactions under different conditions were processed through trajectory clustering and convolutional neural networks. The passenger movement and conflict behavior data were collected from Boeing 737-800 simulation cabin emergency evacuation experiments. These data combined with social force model parameter calibration were used to generate the training dataset. The social-implicit model was trained and validated by using the dataset, and its predictive performance was evaluated in terms of average displacement error (ADE), final displacement error (FDE), average mahalanobis distance (AMD), and average maximum eigenvalue (AME). Analysis results show that the social-implicit model performs well on the constructed emergency evacuation dataset, with an ADE of 0.011 m and an FDE of 0.02 m, representing a 97% reduction compared to the ETH BIWI walking pedestrians dataset and University of Cyprus pedestrian dataset pedestrian datasets. The accuracies of AMD and AMV improve by 72.4% and 94.1%, respectively, indicating that the model excels at capturing passenger trajectory change in narrow environment. In terms of evacuation time, path, speed, and bottleneck position prediction, the model closely aligns with the results of social force model, with evacuation efficiencies of 1.92 and 1.93, respectively. The path prediction can accurately capture the congestion points, with the speed error no exceeding 0.02 m·s^-1, and the bottleneck position error within 0.41 persons per square meter, demonstrating that the model can simulate congestion characteristics during the passenger evacuation. Compared to the social force model, the deep learning model's runtime significantly reduces from 65 s to 0.021 s, and the model size reduces by 78.02%. The social-implicit model provides an efficient and accurate solution for trajectory prediction and performance evaluation in civil aviation emergency evacuation system optimization.
- civil aviation,
- emergency evacuation,
- deep learning,
- trajectory prediction,
- social-implicit model

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References(45)

References

[1]	Aviation Safety Network. ASN aviation safety WikiBase 1999-2021[EB/OL]. 2021, https://asn.flightsafety.org/.
[2]	Airbus. A statistical analysis of commercial aviation accidents 1958-2021[R]. Blagnac: Airbus, 2022.
[3]	MOTEVALLI V, MONAJEMI L, RASSI M. Evaluation and mitigation of aircraft slide evacuation injuries[R]. Washington DC: Transportation Research Board, 2008.
[4]	MARCUS J H. A review of computer evacuation models and their data needs[R]. Washington DC: Civil Aeromedical Institute, 1994.
[5]	DOPART K. Aircraft evacuation testing: research and technology issues[R]. Washington DC: Office of Technology Assessment, 1993.
[6]	Australian Business Traveller. Supersizing the superjumbo: airbus says 1 000 seat A380 due 2020[EB/OL]. 2019, https://www.executivetraveller.com/supersizing-the-superjumbo-1-000-seat-airbus-a380-due-by-2020.
[7]	FENG Wen-chun, LI Wei, ZHANG Tong, et al. Emergency evacuation simulation and test research of civil aircraft complex groups based on the social attribute[J]. Journal of Northwestern Polytechnical University, 2022, 40(4): 853-864. (in Chinese).
[8]	HARRIS D, MUIR H C. Contemporary Issues in Human Factors and Aviation Safety[M]. Hampshire: Ashgate, 2005.
[9]	MUIR H, THOMAS L. Passenger safety and very large transportation aircraft[J]. Aircraft Engineering and Aerospace Technology, 2004, 76(5): 479-486. doi: 10.1108/00022660410555158
[10]	GALEA E R, BLAKE S J, GWYNNE S, et al. The use of evacuation modelling techniques in the design of very large transport aircraft and blended wing body aircraft[J]. The Aeronautical Journal, 2003, 107(1070): 207-218. doi: 10.1017/S0001924000013270
[11]	GALEA E R. Proposed methodology for the use of computer simulation to enhance aircraft evacuation certification[J]. Journal of Aircraft, 2006, 43(5): 1405-1413. doi: 10.2514/1.20937
[12]	ROBBINS C R, MCKEE S. Simulating the evacuation of a commercial airliner[J]. The Aeronautical Journal, 2001, 105(1048): 323-328. doi: 10.1017/S0001924000012203
[13]	XUE Z D, BLOEBAUM C. A particle swarm optimization based aircraft evacuation simulation model-VacateAir[C]//AIAA. 46th AIAA Aerospace Sciences Meeting and Exhibit. Reston: AIAA, 2008: 1-20.
[14]	XUE Z D, BLOEBAUM C. Experimental design based aircraft egress analysis using VacateAir-an aircraft evacuation simulation model[C]//AIAA. 12th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. Reston: AIAA, 2008: 6061.
[15]	JIANG Xing-yu, XIN Yue-lan, LIU Wei-ming. Vehicle detection model based on compressedand lightweight deep neural network[J]. Information Technology, 2020, 44(7): 23-27, 32. (in Chinese).
[16]	WANG Ji-jun, JING Hui, FENG Shu-ming, et al. Object detection method of video monitoring in power warehouse based on Faster R-CNN[J]. Information Technology, 2019, 43(7): 92-96. (in Chinese).
[17]	GIRSHICK R. Fast R-CNN[C]//IEEE. 2015 IEEE International Conference on Computer Vision (ICCV). New York: IEEE, 2015: 1440-1448.
[18]	LUO Jing-yao, HUANG Zheng. Target detection based on CNN classifier and convolution[J]. Information Technology, 2017, 41(9): 101-104, 108. (in Chinese).
[19]	REN S Q, HE K M, GIRSHICK R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137-1149. doi: 10.1109/TPAMI.2016.2577031
[20]	REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, real-time object detection[C]//IEEE. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). New York: IEEE, 2016: 779-788.
[21]	LIU W, ANGUELOV D, ERHAN D, et al. SSD: single shot multibox detector[C]//Springer. European Conference on Computer Vision (ECCV). Berlin: Springer, 2016: 21-37.
[22]	IANDOLA F N, HAN S, MOSKEWICZ M W, et al. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5 MB model size[C]//ICLR. Proceedings of the 5th International Conference on Learning Representations. New York: ICLR, 2016: 1-13.
[23]	SANDLER M, HOWARD A, ZHU M L, et al. MobileNetV2: inverted residuals and linear bottlenecks[C]//IEEE. 2018 IEEE/CFV Conference on Computer Vision and Pattern Recognition. New York: IEEE, 2018: 4510-4520.
[24]	XUAN Qi, LI Fu-xian. Action recognition method based on multiple duration feature fusion[J]. Journal of Zhejiang University of Technology, 2020, 48(4): 361-366. (in Chinese).
[25]	HINTON G E, OSINDERO S, TEH Y W. A fast learning algorithm for deep belief nets[J]. Neural Computation, 2006, 18(7): 1527-1554. doi: 10.1162/neco.2006.18.7.1527
[26]	TAN Zi-yang, GAO Zhong-wen, DENG Yu, et al. The long term prediction of vehicle trajectory based on improved extreme learning machine and depth neural network fusion[J]. Automobile Technology, 2020(11): 16-20. (in Chinese).
[27]	GUPTA A, JOHNSON J, LI F F, et al. Social GAN: socially acceptable trajectories with generative adversarial networks[C]//IEEE. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New York: IEEE, 2018: 2255-2264.
[28]	SADEGHIAN A, KOSARAJU V, SADEGHIAN A, et al. SoPhie: an attentive GAN for predicting paths compliant to social and physical constraints[C]//IEEE. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New York: IEEE, 2019: 1349-1358.
[29]	CARTLIDGE J, GONG S H, BAI R B, et al. Spatio-temporal prediction of shopping behaviours using taxi trajectory data[C]// IEEE. 2018 IEEE 3rd International Conference on Big Data Analysis. New York: IEEE, 2018: 112-116.
[30]	QIAO Shao-jie, JIN Kun, HAN Nan, et al. Trajectory prediction algorithm based on Gaussian mixture model[J]. Journal of Software, 2015, 26(5): 1048-1063. (in Chinese).
[31]	PECHER P, HUNTER M, FUJIMOTO R. Data-driven vehicle trajectory prediction[C]//ACM. Proceedings of the 2016 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation. New York: ACM, 2016: 13-22.
[32]	ZHANG Kai. Learning and prediction based on massive taxi trajectory data[D]. Xi'an: Chang'an University, 2019. (in Chinese).
[33]	MUIR H, COBBETT A. Aircraft evacuation tests—an initial assessment of the influence of various aisle configurations and lighting conditions under different evacuation scenarios[R]. Montreal: Transportation Development Centre, 1996.
[34]	WU Yi-bing. Research on aircraft emergency evacuation simulation based on social force model[D]. Tianjin: Civil Aviation University of China, 2018. (in Chinese).
[35]	DU Yue-juan. Civil aircraft emergency evacuation simulation and analysis of parameter influence[D]. Tianjin: Civil Aviation University of China, 2017. (in Chinese).
[36]	SONG Na. Bottleneck analysis and simulation of emergency evacuation of aircraft crew[D]. Tianjin: Civil Aviation University of China, 2020. (in Chinese).
[37]	PELLEGRINI S, ESS A, SCHINDLER K, et al. You'll never walk alone: modeling social behavior for multi-target tracking[C]//IEEE. 2009 IEEE 12th International Conference on Computer Vision. New York: IEEE, 2009: 261-268.
[38]	LERNER A, CHRYSANTHOU Y, LISHINSKI D. Crowds by example[J]. Computer Graphics Forum, 2007, 26(3): 655-664. doi: 10.1111/j.1467-8659.2007.01089.x
[39]	HELBING D, JOHANSSON A. Encyclopedia of Complexity and Systems Science[M]. New York: Springer, 2013.
[40]	MOHAMED A, ZHU D Y, VU W, et al. Social-implicit: rethinking trajectory prediction evaluation and the effectiveness of implicit maximum likelihood estimation[C]//Springer. Proceedings of the European Conference on Computer Vision. Berlin: Springer, 2022: 463-479.
[41]	ALAHI A, GOEL K, RAMANATHAN V, et al. Social LSTM: human trajectory prediction in crowded spaces[C]// IEEE. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). New York: IEEE, 2016: 961-971.
[42]	IVANOVIC B, PAVONE M. The trajectron: probabilistic multi-agent trajectory modeling with dynamic spatiotemporal graphs[C]//IEEE 2019 IEEE/CVF International Conference on Computer Vision (ICCV). New York: IEEE, 2019: 2375-2384.
[43]	MONTI A, BERTUGLI A, CALDERARA S, et al. DAG-net: double attentive graph neural network for trajectory forecasting[C]//IEEE. 2020 25th International Conference on Pattern Recognition (ICPR). New York: IEEE, 2021: 2804-2810.
[44]	MOHAMED A, QIAN K, ELHOSEINY M, et al. Social- STGCNN: a social spatio-temporal graph convolutional neural network for human trajectory prediction[C]//IEEE. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). New York: IEEE, 2020: 14424-14432.
[45]	ZHANG J, KLINGSCH W, SCHADSCHNEIDER A, et al. Transitions in pedestrian fundamental diagrams of straight corridors and T-junctions[J]. Journal of Statistical Mechanics: Theory and Experiment, 2011, 2011(6): P06004.

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Evacuation trajectory prediction of passengers in transport aircraft based on social-implicit model

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