Study on Factors for Passenger Risk in Railway Vehicle

doi:10.15683/kosdi.2021.12.31.733

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2021 Vol.17, Issue 4 Preview Page Next Page

Original Article

Study on Factors for Passenger Risk in Railway Vehicle 철도차량내 승객 위험요소 선정 연구: 박원희¹*ㆍ박성준²ㆍ김효진³ㆍ김한샘⁴ㆍ오세찬⁵
Won-Hee Park¹*, Sung-Joon Park², Hyo-Jin Kim³, HanSaem Kim⁴, Sechan Oh⁵; ¹Principal Researcher, Korea Railroad Research Institute, Uiwang, Republic of Korea
²Senior Consultant, Knowledgeworks, Seoul, Republic of Korea
³Student, Korea National University of Transportation, Uiwang, Republic of Korea
⁴Researcher, Kakao Enterprise, Seongnam, Republic of Korea
⁵Senior Researcher, Korea Railroad Research Institute, Uiwang, Republic of Korea

31 December 2021. pp. 14

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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

연구목적: 본 연구는 철도차량 내 승객 이상행동 및 위험상황 중 중요한 위험요소를 도출하기 위하여 승객 및 운용기관 종사자의 의견을 조사하고 분석을 하였다. 연구방법: 일반국민 20~60대 성인남녀 1,000명 및 전국 11개 기관의 종사자 429명에게 설문조사를 수행하였다. 일반국민에게는 지하철, 일반철도, 고속철도 등으로 구분하여 철도차량 내 발생 가능한 위험 상황에 대한 설문을 조사하였으며 운영기관 종사자들에게는 지하철 내의 위험 상황에 대한 설문을 조사하였다. 연구결과: 승객위험요소 발생에 대한 빈도 및 중요도를 판단근거로 선정된 철도차량내 승객 위험요소(이상행동 및 위험 상황) 중 주요 위험요소로 ‘차량 문끼임’, ‘성추행’, ‘주취행동’, ‘싸움/폭행’, ‘배회’. ‘마스크 미착용’으로 선정하였다. 결론: 승객에게 영향을 미치는 주요 위험요소는 일반 국민 및 철도운영기관의 종사자들의 설문조사를 통하여 선정되었다. 본 연구에서 얻은 결과인 철도차량내 주요 위험요소 발생시 신속하며 지속적으로 감지할 수 있는 AI 기술이 적용된 CCTV 감지 시스템이 개발될 예정이다.

키워드

철도차량

지하철

승객

위험요소

설문조사

CCTV

철도운영기관

References

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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.
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. 10.3390/sym12050744
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. 10.1109/ACPR.2015.7486569
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Ministry of Land, Infrastructure and Transport (2021) Railroad Safety Act. Korea.
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. 10.1109/ICCV.2017.590
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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. 10.15683/kosdi.2015.11.3.337
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. 10.15683/kosdi.2015.11.3.356
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Information

Publisher :The Korean Society of Disaster Information
Publisher(Ko) :한국재난정보학회
Journal Title :Journal of the Society of Disaster Information
Journal Title(Ko) :한국재난정보학회논문집
Volume : 17
No :4
Pages :14
DOI :https://doi.org/10.15683/kosdi.2021.12.31.733

[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. 10.1109/CVPR.2017.143

[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.

[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. 10.3390/sym12050744

[5] 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. 10.1109/ACPR.2015.7486569

[6] Fan, H., Xiong, B., Mangalam, K., Li, Y., Yan, Z., Malik, J., Feichtenhofer, C. (2021). "Multiscale vision transformers." arXiv preprint arXiv:2104.11227.

[7] 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. 10.1109/CVPR42600.2020.00028

[8] 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. 10.1109/ICCV.2019.00630

[9] 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.

[10] 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. 10.1109/CVPR.2019.00371

[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] Ministry of Land, Infrastructure and Transport (2021) Railroad Safety Act. Korea.

[13] 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. 10.1109/ICCV.2017.590

[14] 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. 10.1109/CVPR.2016.91

[15] 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. 10.15683/kosdi.2015.11.3.337

[16] 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. 10.15683/kosdi.2015.11.3.356

[17] 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.

[18] Yeonhap News Agency (2020). https://www.yna.co.kr/view/AKR20200922092800530.

Journal of the Society of Disaster InformationISSN:1976-2208(Print) 2671-5287(Online)한국재난정보학회

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