Signal game model of intelligent joint defense security inspection in urban rail transit hubs

LI Delong, LIU Dehai

Systems Engineering - Theory & Practice ›› 2022, Vol. 42 ›› Issue (12) : 3363-3380.

PDF(1352 KB)
PDF(1352 KB)
Systems Engineering - Theory & Practice ›› 2022, Vol. 42 ›› Issue (12) : 3363-3380. DOI: 10.12011/SETP2021-2715

Signal game model of intelligent joint defense security inspection in urban rail transit hubs

  • LI Delong1, LIU Dehai2
Author information +
History +

Abstract

In the construction process of the new urban rail transit intelligent security inspection mode integrating face recognition technology and white list security inspection system, there still exist some scientific difficulties such as designing security inspection network based on joint defense mechanism, designing security inspection network security evaluation mechanism and designing security inspection signal strategy considering the attackers' emotion. Firstly, this paper proposes a two-level joint defense model of urban rail transit hubs, which takes the face recognition system as the first level and the white list channel as the second level. Then, a signaling game model of terrorist and riot related security inspection considering the attacker's emotion is constructed, and three refined Bayesian equilibrium paths considering efficiency and safety and the optimal signal release strategy of the security inspection departments are obtained. The study finds that the greater the success rate of attackers avoiding face recognition through camouflage strategy, the more the security inspection departments should strengthen the detection ability for contraband, rather than continuously improve the joint defense ability for passenger identity attributes. The penetration strategy of attackers into the white list channel or ordinary channel directly affects the joint defense strategy of the security inspection departments. When the white list channel is selected, the efficiency of alarm response time and the level of information synchronization complement each other; On the contrary, the efficiency of alarm response time is complementary to the safety of ordinary channels. The formulation of security inspection signal strategy should fully consider the attackers' emotion, the proportion of low-capacity security inspection departments, the attackers' belief in the proportion of low-capacity security inspection departments, etc. for example, when the proportion of low-capacity security inspection departments is relatively low, the security inspection departments have the premise of releasing separated signals, otherwise they should release high-capacity mixed signals uniformly.

Key words

security inspection / signaling game / counter-terrorism / face recognition / white list system

Cite this article

Download Citations
LI Delong , LIU Dehai. Signal game model of intelligent joint defense security inspection in urban rail transit hubs. Systems Engineering - Theory & Practice, 2022, 42(12): 3363-3380 https://doi.org/10.12011/SETP2021-2715

References

[1] 李德龙,刘德海. 基于白名单的地铁涉恐防爆安检序贯博弈模型[J].系统工程理论与实践, 2021, 41(11):2975-2991. Li D L, Liu D H. Sequential game model of subway security inspection in terrorism related explosion protection based on white list system[J]. Systems Engineering-Theory & Practice, 2021, 41(11):2975-2991.
[2] 李德龙, 刘德海, 王雷. 引入信号装置的地铁安检反恐博弈模型[J].系统工程理论与实践, 2020, 40(1):134-149.Li D L, Liu D H, Wang L. Anti-terrorism game model of subway security screening with signal device[J]. Systems Engineering-Theory & Practice, 2020, 40(1):134-149.
[3] McClain N. The horizons of technological control:Automated surveillance in the New York subway[J]. Information, Communication & Society, 2018, 21(1):46-62.
[4] 宋优才. 基于"白名单"的城市轨道交通快速安检方案构想[J].隧道与轨道交通, 2020, 34(4):6-8.Song Y C. Conception of ‘Whitelist’ based rapid security inspection scheme of urban rail transit[J]. Tunnel and Rail Transit, 2020, 34(4):6-8.
[5] 李德龙. 地铁涉恐防爆安检策略的博弈分析[D]. 大连:东北财经大学, 2021. Li D L. Game analysis on the strategy of subway anti-terrorism security inspection[D]. Dalian:Dongbei University of Finance and Economics, 2021.
[6] Gupta S, Starr M K, Zanjirani Farahani R, et al. Prevention of terrorism-An assessment of prior POM work and future potentials[J]. Production and Operations Management, 2020, 29(7):1789-1815.
[7] Payyappalli V M, Zhuang J, Jose V. Deterrence and risk preferences in a sequential attacker-defender game with continuous defense effort[J]. Risk Analysis, 2017, 37(11):2229-2245.
[8] 王广民,高自友,徐猛,等.弹性需求下网络设计问题和电子路票问题研究[J].管理科学学报, 2015, 18(4):38-48. Wang G M, Gao Z Y, Xu M, et al. The combined model and relaxation algorithm for continuous network design problem with the second-best credits charging under elastic demand[J]. Journal of Management Sciences in China, 2015, 18(4):38-48.
[9] Song C, Zhuang J. Two-stage security screening strategies in the face of strategic applicants, congestions and screening errors[J]. Annals of Operations Research, 2017, 258(2):237-262.
[10] Song C, Zhuang J. N-stage security screening strategies in the face of strategic applicants[J]. Reliability Engineering & System Safety, 2017, 165(C):292-301.
[11] Stotz T, Bearth A, Ghelfi S M, et al. Keep the status quo:Randomization-based security checks might reduce crime deterrence at airports[J]. Journal of Risk Research, 2021, 24(12):1589-1604.
[12] 陈晓红,徐敏婕,陈武华. 考虑成本、等待时间和安全水平的分类安检模式研究[J]. 运筹与管理, 2021, 30(7):35-43.Chen X H, Xu M J, Chen W H. Research on classified security inspection mode considering cost, waiting time and security level[J]. Operations Research and Management Science, 2021, 30(7):35-43.
[13] Li Y, Gao X, Xu Z, et al. Network-based queuing model for simulating passenger throughput at an airport security checkpoint[J]. Journal of Air Transport Management, 2018, 66(C):13-24.
[14] 冯文刚, 姜兆菲璠. 基于民航旅客分级分类方法的差异化安检和旅客风险演化研究[J]. 数据分析与知识发现, 2020, 4(12):105-119. Feng W G, Jiang Z F F. Improving security checks and passenger risk evaluation with classification of airline passengers[J]. Data Analysis and Knowledge Discovery, 2020, 4(12):105-119.
[15] Bakshi N, Pinker E. Public warnings in counterterrorism operations:Managing the "cry-wolf" effect when facing a strategic adversary[J]. Operations Research, 2018, 66(4):977-993.
[16] Zhai Q, Peng R, Zhuang J. Defender-attacker games with asymmetric player utilities[J]. Risk Analysis, 2020, 40(2):408-420.
[17] 刘德海, 鲍雪言, 王谢宁. 恐怖袭击事件中悲观乐观情绪如何影响博弈均衡结果[J].中国管理科学, 2017, 25(10):80-88.Liu D H, Bao X Y, Wang X N. How does the pessimistic or optimistic emotion influece the game equilibrium outcome in incidnets of violence and terrorism[J]. Chinese Journal of Management Science, 2017, 25(10):80-88.
[18] 汪广龙. 治安防控体系演化的组织机制——基于"打防并举"到"管理服务"变迁历程的研究[J].公共管理学报, 2020, 17(2):128-140+74.Wang G L. Organizational mechanism of the public security system evolution:Study on the transforming from "prevention and control" to "manage and service"[J]. Journal of Public Management, 2020, 17(2):128-140+74.
[19] 刘忠轶,胡晨望,谭坤, 等. 基于排队论的反恐警力优化配置策略研究[J].数据分析与知识发现, 2018, 2(10):37-45.Liu Z Y, Hu C W, Tan K, et al. Optimizing anti-terrorist policing with queueing theory[J]. Data Analysis and Knowledge Discovery, 2018, 2(10):37-45.
[20] 罗常伟,於俊,于灵云,等.三维人脸识别研究进展综述[J].清华大学学报(自然科学版), 2021, 61(1):80-91.Luo C W, Yu J, Yu L Y, et al. Overview of research progress on 3-D face recognition[J]. Journal of Tsinghua University (Science and Technology), 2021, 61(1):80-91.
[21] 冯卫国.总体国家安全观与反恐对策思考[J].理论探索, 2017, 34(5):109-114.Feng W G. Overall national security concept and counter-terrorism measures[J]. Theoretical Exploration, 2017, 34(5):109-114.
[22] Bakshi N, Kim S H, Savva N. Signaling new product reliability with after-sales service contracts[J]. Management Science, 2015, 61(8):1812-1829.
[23] Hofmann D C. How "alone" are lone-actors? Exploring the ideological, signaling, and support networks of lone-actor terrorists[J]. Studies in Conflict & Terrorism, 2018, 43(7):1-22.
[24] Knol A, Sharpanskykh A, Janssen S. Analyzing airport security checkpoint performance using cognitive agent models[J]. Journal of Air Transport Management, 2019, 75(5):39-50.
[25] Liu R, Li S, Yang L. Collaborative optimization for metro train scheduling and train connections combined with passenger flow control strategy[J]. Omega, 2020, 90:101990.

Funding

National Natural Science Foundation of China (71874024, 71971045); Natural Science Foundation of Inner Mongolia Autonomous Region of China (2022QN07003); Resource Utilization and Environmental Protection Coordinated Development Academician Expert Workstation in the North of China (2021NCDYSZJGZZ-001); Program for Innovative Research Team in Universities of Inner Mongolia Autonomous Region of China (NMGIRT2202)
PDF(1352 KB)

685

Accesses

0

Citation

Detail

Sections
Recommended

/