Application of Edge Intelligence in Rail Transit: Prospects and Future Outlook

ZHU Li; GONG Taiyuan; LIANG Hao; TANG Tao; WANG Xi; WANG Hongwei

doi:10.11999/JEIT220116

Volume 45 Issue 4

Apr. 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(4): 1514-1528

ZHU Li, GONG Taiyuan, LIANG Hao, TANG Tao, WANG Xi, WANG Hongwei. Application of Edge Intelligence in Rail Transit: Prospects and Future Outlook[J]. Journal of Electronics & Information Technology, 2023, 45(4): 1514-1528. doi: 10.11999/JEIT220116

Citation:

ZHU Li, GONG Taiyuan, LIANG Hao, TANG Tao, WANG Xi, WANG Hongwei. Application of Edge Intelligence in Rail Transit: Prospects and Future Outlook[J]. Journal of Electronics & Information Technology, 2023, 45(4): 1514-1528. doi: 10.11999/JEIT220116

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Application of Edge Intelligence in Rail Transit: Prospects and Future Outlook

doi: 10.11999/JEIT220116

School of Electronic Information Engineering, Beijing Jiaotong University, Beijing 100044, China

Funds: The National Natural Science Foundation of China (61973026)

Received Date: 2022-01-27
Rev Recd Date: 2022-06-20

Available Online: 2202-06-29

Publish Date: 2023-04-10

Abstract

Abstract

As an emerging technology, edge intelligence is receiving extensive attention from scholars at home and abroad. As a combination of artificial intelligence technology and edge computing technology, it is expected to promote the deployment of artificial intelligence technology in various industries and accelerate the process of industrial intelligence. In this paper the basic principles, system architecture, and comparative advantages of edge intelligence technology, and sorts out the research status of edge intelligence technology at home and abroad are first introduced. The application prospects of the life cycle, the application of edge intelligence technology in the whole life cycle of rail transit process management and control, construction site data collection and analysis, information sharing, intelligent operation and maintenance, intelligent scheduling, automatic driving system, train coordination control, and transformation and upgrading are described in detail. Then the designs and implements an edge intelligent platform under the background of rail transit intelligent operation control, and tests the functions and performance of edge intelligence applications based on deep learning and reinforcement learning are discussed. Finally, the problems and challenges in the application of edge intelligence technology to the field of rail transit are summarized. The research in this paper is expected to provide a useful reference and practical basis for edge intelligence applications to the field of rail transit.
- Edge intelligence,
- Rail transportation,
- Artificial intelligence,
- Edge computing

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References(35)

References

[1]	SATYANARAYANAN M, BAHL P, CACERES R, et al. The case for VM-based cloudlets in mobile computing[J]. IEEE Pervasive Computing, 2009, 8(4): 14–23. doi: 10.1109/MPRV.2009.82
[2]	DENG Shuiguang, ZHAO Hailiang, FANG Weijia, et al. Edge intelligence: The confluence of edge computing and artificial intelligence[J]. IEEE Internet of Things Journal, 2020, 7(8): 7457–7469. doi: 10.1109/JIOT.2020.2984887
[3]	ZHOU Zhi, CHEN Xu, LI En, et al. Edge intelligence: Paving the last mile of artificial intelligence with edge computing[J]. Proceedings of the IEEE, 2019, 107(8): 1738–1762. doi: 10.1109/JPROC.2019.2918951
[4]	PELTONEN E, BENNIS M, CAPOBIANCO M, et al. 6G white paper on edge intelligence[J]. arXiv preprint arXiv: 2004.14850, 2020.
[5]	朱卫国. 城市轨道交通综述[J]. 城市车辆, 2001(3): 37–40. ZHU Weiguo. An overview of urban rail transportation[J]. Urban Vehicles, 2001(3): 37–40.
[6]	张殿业, 金键, 杨京帅. 城市轨道交通安全研究体系[J]. 都市快轨交通, 2004, 17(4): 1–3. doi: 10.3969/j.issn.1672-6073.2004.04.001 ZHANG Dianye, JIN Jian, and YANG Jingshuai. The safety research system of urban rail transit[J]. Urban Rapid Rail Transit, 2004, 17(4): 1–3. doi: 10.3969/j.issn.1672-6073.2004.04.001
[7]	史天运. 中国高速铁路信息化现状及智能化发展[J]. 科技导报, 2019, 37(6): 53–59. SHI Tianyun. Present situation of wide applications of information and intelligence in the field of high-speed railway in China[J]. Science &Technology Review, 2019, 37(6): 53–59.
[8]	王同军. 智能铁路总体架构与发展展望[J]. 铁路计算机应用, 2018, 27(7): 1–8. doi: 10.3969/j.issn.1005-8451.2018.07.003 WANG Tongjun. Overall framework and development prospect of intelligent railway[J]. Railway Computer Application, 2018, 27(7): 1–8. doi: 10.3969/j.issn.1005-8451.2018.07.003
[9]	刘芽, 刘占英, 麻永华, 等. 基于云计算技术的城市轨道交通信息化平台发展探索[J]. 现代城市轨道交通, 2019(9): 121–125. LIU Ya, LIU Zhanying, MA Yonghua, et al. Exploration on development of urban rail transit informatization platform based on cloud computing technology[J]. Modern Urban Transit, 2019(9): 121–125.
[10]	张春杰, 武智博, 张硕桐. 物联网及人工智能技术在城市轨道交通综合监控系统中的应用探究[J]. 电子世界, 2020(7): 55–56. doi: 10.19353/j.cnki.dzsj.2020.07.029 ZHANG Chunjie, WU Zhibo, and ZHANG Shuotong. Exploring the application of Internet of things and artificial intelligence technology in urban rail transit integrated monitoring system[J]. Electronics World, 2020(7): 55–56. doi: 10.19353/j.cnki.dzsj.2020.07.029
[11]	魏秀琨, 所达, 魏德华, 等. 机器视觉在轨道交通系统状态检测中的应用综述[J]. 控制与决策, 2021, 36(2): 257–282. doi: 10.13195/j.kzyjc.2020.1199 WEI Xiukun, SUO Da, WEI Dehua, et al. A survey of the application of machine vision in rail transit system inspection[J]. Control and Decision, 2021, 36(2): 257–282. doi: 10.13195/j.kzyjc.2020.1199
[12]	周超, 林湛, 李樊, 等. 城市轨道交通视频监控系统云边协同技术应用研究[J]. 铁道运输与经济, 2020, 42(12): 106–110,125. doi: 10.16668/j.cnki.issn.1003-1421.2020.12.18 ZHOU Chao, LIN Zhan, LI Fan, et al. Cloud-edge collaboration technology of CCTV system for urban rail transit[J]. Railway Transport and Economy, 2020, 42(12): 106–110,125. doi: 10.16668/j.cnki.issn.1003-1421.2020.12.18
[13]	施巍松, 孙辉, 曹杰, 等. 边缘计算: 万物互联时代新型计算模型[J]. 计算机研究与发展, 2017, 54(5): 907–924. doi: 10.7544/issn1000-1239.2017.20160941 SHI Weisong, SUN Hui, CAO Jie, et al. Edge computing-an emerging computing model for the internet of everything era[J]. Journal of Computer Research and Development, 2017, 54(5): 907–924. doi: 10.7544/issn1000-1239.2017.20160941
[14]	O'LEARY D E. Artificial intelligence and big data[J]. IEEE Intelligent Systems, 2013, 28(2): 96–99. doi: 10.1109/MIS.2013.39
[15]	ZHU LI, YU F R, WANG Yige, et al. Big data analytics in intelligent transportation systems: A survey[J]. IEEE Transactions on Intelligent Transportation Systems, 2019, 20(1): 383–398. doi: 10.1109/TITS.2018.2815678
[16]	MICHALSKI R S, CARBONELL J G, and MITCHELL T M. Machine Learning: An Artificial Intelligence Approach[M]. Berlin: Springer Science & Business Media, 2013.
[17]	MENG Yan and LIU Xiyu. Application of K-means algorithm based on ant clustering algorithm in macroscopic planning of highway transportation hub[C]. 2007 First IEEE International Symposium on Information Technologies and Applications in Education, Kunming, China, 2007: 483–488.
[18]	NATH R P D, LEE H J, CHOWDHURY N K, et al. Modified K-means clustering for travel time prediction based on historical traffic data[C]. 14th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, Cardiff, UK, 2010: 511–521.
[19]	HAYDARI A and YILMAZ Y. Deep reinforcement learning for intelligent transportation systems: A survey[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(1): 11–32. doi: 10.1109/TITS.2020.3008612
[20]	ABDULJABBAR R, DIA H, LIYANAGE S, et al. Applications of artificial intelligence in transport: An overview[J]. Sustainability, 2019, 11(1): 189. doi: 10.3390/su11010189
[21]	LECUN Y and RANZATO M. Deep learning tutorial[C]. Tutorials in International Conference on Machine Learning (ICML’13), Atlanta, USA, 2013: 1–29.
[22]	ATZORI L, IERA A, and MORABITO G. The internet of things: A survey[J]. Computer Networks, 2010, 54(15): 2787–2805. doi: 10.1016/j.comnet.2010.05.010
[23]	OUGHTON E J, LEHR W, KATSAROS K, et al. Revisiting wireless internet connectivity: 5G vs Wi-Fi 6[J]. Telecommunications Policy, 2021, 45(5): 102127. doi: 10.1016/j.telpol.2021.102127
[24]	CHEN Xu, JIAO Lei, LI Wenzhong, et al. Efficient multi-user computation offloading for mobile-edge cloud computing[J]. IEEE/ACM Transactions on Networking, 2016, 24(5): 2795–2808. doi: 10.1109/TNET.2015.2487344
[25]	KAI Caihong, ZHOU Hao, YI Yibo, et al. Collaborative cloud-edge-end task offloading in mobile-edge computing networks with limited communication capability[J]. IEEE Transactions on Cognitive Communications and Networking, 2021, 7(2): 624–634. doi: 10.1109/TCCN.2020.3018159
[26]	LI En, ZHOU Zhi, and CHEN Xu. Edge intelligence: On-demand deep learning model co-inference with device-edge synergy[C]. The 2018 Workshop on Mobile Edge Communications, Budapest, Hungary: ACM, 2018: 31–36.
[27]	HAN Song, POOL J, TRAN J, et al. Learning both weights and connections for efficient neural networks[C]. The 28th International Conference on Neural Information Processing Systems, Montreal, Canada, 2015: 1135–1143.
[28]	LI Song, ZHAO Hongli, and MA Jinmin. An edge computing-enabled train obstacle detection method based on YOLOv3[J]. Wireless Communications and Mobile Computing, 2021, 2021: 7670724. doi: 10.1155/2021/7670724
[29]	TANG Jie, LIU Shaoshan, LIU Liangkai, et al. LoPECS: A low-power edge computing system for real-time autonomous driving services[J]. IEEE Access, 2020, 8: 30467–30479. doi: 10.1109/ACCESS.2020.2970728
[30]	DAI Yueyue, XU Du, MAHARJAN S, et al. Joint load balancing and offloading in vehicular edge computing and networks[J]. IEEE Internet of Things Journal, 2019, 6(3): 4377–4387. doi: 10.1109/JIOT.2018.2876298
[31]	GARCIA M H C, MOLINA-GALAN A, BOBAN M, et al. A tutorial on 5G NR V2X communications[J]. IEEE Communications Surveys & Tutorials, 2021, 23(3): 1972–2026. doi: 10.1109/COMST.2021.3057017
[32]	李迎九. 智能建造技术在铁路建设管理中的应用探索[J]. 中国铁路, 2018(5): 1–7. doi: 10.19549/j.issn.1001-683x.2018.05.001 LI Yingjiu. The application of intelligent building technology in railway construction management[J]. China Railway, 2018(5): 1–7. doi: 10.19549/j.issn.1001-683x.2018.05.001
[33]	李得伟, 张天宇, 周玮腾, 等. 轨道交通大数据运用现状及发展趋势研究[J]. 都市快轨交通, 2016, 29(6): 1–7. doi: 10.3969/j.issn.1672-6073.2016.06.001 LI Dewei, ZHANG Tianyu, ZHOU Weiteng, et al. State-of-the-art and trend analysis of big data application in rail transit[J]. Urban Rapid Rail Transit, 2016, 29(6): 1–7. doi: 10.3969/j.issn.1672-6073.2016.06.001
[34]	端嘉盈, 沈海燕, 李智. 边缘计算在铁路“智能车站”物联网中的应用研究[J]. 物联网技术, 2020, 10(10): 53–56,61. doi: 10.16667/j.issn.2095-1302.2020.10.015 DUAN Jiaying, SHEN Haiyan, and LI Zhi. Application research of edge computing in railway "smart station" internet of things[J]. Internet of Things Technologies, 2020, 10(10): 53–56,61. doi: 10.16667/j.issn.2095-1302.2020.10.015
[35]	王悉. 基于机器学习的重载列车智能驾驶方法研究[D]. [博士论文], 北京交通大学, 2017. WANG Xi. Machine learning based intelligent operation methods for heavy haul train[D]. [Ph. D. dissertation], Beijing Jiaotong University, 2017.