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http://dx.doi.org/10.15683/kosdi.2021.12.31.733

Study on Factors for Passenger Risk in Railway Vehicle  

Park, Won-Hee (Korea Railroad Research Institute)
Park, Sung-Joon (Knowledgeworks)
Kim, Hyo-Jin (Korea National University of Transportation)
Kim, HanSaem (Kakao Enterprise)
Oh, Sechan (Korea Railroad Research Institute)
Publication Information
Journal of the Society of Disaster Information / v.17, no.4, 2021 , pp. 733-746 More about this Journal
Abstract
Purpose: This study was conducted for the purpose of selecting important events from among various events that may pose a risk to railway passengers. For this purpose, opinions of various railroad vehicle passengers and railway operator workers were investigated and analyzed. Method: The survey was conducted on 1,000 men and women in their 20s and 60s and 429 workers at 11 company across the country. A survey was conducted on the dangerous situations that may occur in subways, general railroads and high-speed rail vehicles targeting passengers. For railway operator workers, the questionnaire is limited to subway vehicles. Result: Among the passenger risk factors(abnormal behavior and dangerous situations) selected based on the frequency and importance of occurrence of passenger risk factors, the main risk factors are selected 'car door jamming', 'sexual harassment', 'intoxicating behavior', 'fighting' /assault', 'wandering around', and 'not wearing a mask'. Conclusion: The major risk factors affecting passengers were selected by surveying passengers and railway operators. we plan to develop a CCTV detection system with AI technology that can quickly and continuously detect the major risk factors of railway vehicles selected as a result of this study.
Keywords
Railway Vehicle; Subway; Passenger; Risk; Survey; CCTV; Railway Operator;
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1 Bertasius, G., Wang, H., Torresani, L. (2021). "Is space-time attention all you need for video understanding?" arXiv preprint arXiv:2102.05095.
2 Cao, Z., Simon, T., Wei, S.E., Sheikh, Y. (2017). "Realtime multi-person 2d pose estimation using part affinity fields." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, pp. 7291-7299.
3 Chang, J.-Y., Hong, S.-M., Son, D., Yoo, H., Ahn, H.-W. (2019). "Development of real-time video surveillance system using the intelligent behavior recognition technique." The Journal of the Institute of Internet, Broadcasting and Communication, Vol. 19, No. 2, pp. 161-168.   DOI
4 Chen, W., Jiang, Z., Guo, H., Ni, X. (2020). "Fall detection based on key points of human-skeleton using openpose." Symmetry, Vol. 12, No. 5, p. 744.   DOI
5 Fan, H., Xiong, B., Mangalam, K., Li, Y., Yan, Z., Malik, J., Feichtenhofer, C. (2021). "Multiscale vision transformers." arXiv preprint arXiv:2104.11227.
6 Feichtenhofer, C. (2020). "X3d: Expanding architectures for efficient video recognition." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA, pp. 203-213.
7 Kim, J.-H., Choi, J.-H., Park, Y.-H., Nasridinov, A. (2021). "Abnormal situation detection on surveillance video using object detection and action recognition." Journal of Korea Multimedia Society, Vol. 24, No. 2, pp. 186-198.   DOI
8 Li, M., Chen, S., Chen, X., Zhang, Y., Wang, Y., Tian, Q. (2019). "Actional-structural graph convolutional networks for skeleton-based action recognition." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA, pp. 3595-3603.
9 Ministry of Land, Infrastructure and Transport (2021) Railroad Safety Act. Korea.
10 Seo, G., Kim, D., Choi, Y. (2015). "Disaster risk analysis of domestic public institutions 1 - Focusing on simulation training and an attitude survey -." Journal of The Korean Society of Disaster Information, Vol. 11, No. 3, pp. 337-345.   DOI
11 Liu, Z., Ning, J., Cao, Y., Wei, Y., Zhang, Z., Lin, S., Hu, H. (2021). "Video swin transformer." arXiv preprint arXiv:2106.13230.
12 Seo, G.-D., Kim, D.-H., Choi, Y.-C. (2015). "Disaster risk analysis of domestic public institutions 2 - Focusing on analysis of risk factors -." Journal of The Korean Society of Disaster Information, Vol. 11, No. 3, pp. 356-364.   DOI
13 Yeonhap News Agency (2020). https://www.yna.co.kr/view/AKR20200922092800530.
14 Du, Y., Fu, Y., Wang, L. (2015). "Skeleton based action recognition with convolutional neural network." Proceedings of the 3rd IAPR Asian Conference on Pattern Recognition (ACPR), Kuala Lumpur, Malaysia, pp. 579-583.
15 Qiu, Z., Yao, T., Mei, T. (2017). "Learning spatio-temporal representation with pseudo-3d residual networks." Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy, pp. 5533-5541.
16 Feichtenhofer, C., Fan, H., Malik, J., He, K. (2019). "Slowfast networks for video recognition." Proceedings of the IEEE/CVF International Conference on Computer Vision, Seoul, Korea (South), pp. 6202-6211.
17 Redmon, J., Divvala, S., Girshick, R., Farhadi, A. (2016). "You only look once: Unified, real-time object detection." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, pp. 779-788.
18 Yan, S., Xiong, Y., Lin, D. (2018). "Spatial temporal graph convolutional networks for skeleton-based action recognition." Thirty-second AAAI Conference on Artificial Intelligence, New Orleans, Lousiana, USA, pp. 7444- 7452.