Dynamic Simulation of Urban Agglomeration Passenger Transport Network Vulnerability Based on Multi-agent

doi:10.16182/j.issn1004731x.joss.22-0200

Abstract

Abstract:

Research on vulnerability of comprehensive passenger transportation network in urban agglomerations helps to ensure the transportation efficiency of intercity travel. A comprehensive passenger transport network model of urban agglomeration is built based on multi-layer complex network theory. Urban transportation transfer factors are considered, actual passenger flow is used to calibrate the station load and capacity and a dynamic model of network cascading failure is constructed. Multi-agents are used to simulate the actual passenger flow, Dijkstra algorithm is used to find the shortest path, and two time-dimensional vulnerability evaluation indicators are proposed. MATLAB is used to carry out the dynamic simulation of Hubao-Eyu urban agglomeration. The results show that most train stations have obvious range and time, impact on the network after being attacked, but the duration is not significantly different from the bus stations. The impact on the network of different sites being attacked has no obvious relationship with the duration.

Key words: urban agglomeration transport, passenger traffic network, multi-agent, vulnerability, dynamic simulation

CLC Number:

TP391

Chengbing Li, Yunfei Li, Peng Wu. Dynamic Simulation of Urban Agglomeration Passenger Transport Network Vulnerability Based on Multi-agent[J]. Journal of System Simulation, 2023, 35(6): 1183-1190.

Figures/Tables 8

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Table 1

Fig. 7

References 15

1	黄敏, 钮中铭, 张小兰, 等. 基于GIS-T的交通仿真路网构建研究[J]. 系统仿真学报, 2017, 29(3): 531-536.
	Huang Min, Niu Zhongming, Zhang Xiaolan, et al. Research of the Construction of GIS-T Traffic Simulation Road Network[J]. Journal of System Simulation, 2017, 29(3): 531-536.
2	何兆成, 林炫华, 聂佩林, 等. 考虑展宽设计的中观交通仿真模型及其标定[J]. 系统仿真学报, 2021, 33(4): 781-791.
	He Zhaocheng, Lin Xuanhua, Nie Peilin, et al. Mesoscopic Traffic Simulation Model and Calibration Considering Stretching-segment Design[J]. Journal of System Simulation, 2021, 33(4): 781-791.
3	姜安培, 吴海俊, 赵慧. 基于双层模型与仿真的区域交通组织优化研究[J]. 武汉理工大学学报(交通科学与工程版), 2022, 46(6): 1008-1012.
	Jiang Anpei, Wu Haijun, Zhao Hui. Research on Optimization of Regional Traffic Organization Based on Double-layer Model and Simulation[J]. Journal of Wuhan University of Technology(Transportation Science & Engineering), 2022, 46(6): 1008-1012.
4	李成兵, 张帅, 杨志成, 等. 蓄意攻击下城市群客运交通网络级联抗毁性仿真[J]. 交通运输系统工程与信息, 2019, 19(2): 14-21.
	Li Chengbing, Zhang Shuai, Yang Zhicheng, et al. Invulnerability Simulation in Urban Agglomeration Passenger Traffic Network under Targeted Attacks[J]. Journal of Transportation Systems Engineering and Information Technology, 2019, 19(2): 14-21.
5	周天星, 毛剑楠, 刘澜, 等. 城市群公路客运通道与城市交通网络衔接模式选择模型[J]. 交通运输系统工程与信息, 2018, 18(3): 22-28.
	Zhou Tianxing, Mao Jiannan, Liu Lan, et al. Connection Model of Highway Passenger Transportation Corridor and Urban Transit Network in Metropolitan Area[J]. Journal of Transportation Systems Engineering and Information Technology, 2018, 18(3): 22-28.
6	Lu Qingchang, Xu Pengcheng, Zhang Jingxiao. Infrastructure-based Transportation Network Vulnerability Modeling and Analysis[J]. Physica A: Statistical Mechanics and its Applications, 2021, 584: 126350.
7	张光远, 张帆, 刘泳博. 成渝地区城际铁路网络特性与脆弱性分析[J]. 铁道运输与经济, 2021, 43(7): 36-42.
	Zhang Guangyuan, Zhang Fan, Liu Yongbo. Characteristics and Vulnerabilities of Intercity Railway Network in Chengdu-chongqing Region[J]. Railway Transport and Economy, 2021, 43(7): 36-42.
8	Malandri C, Mantecchini L, Paganelli F, et al. Public Transport Network Vulnerability and Delay Distribution among Travelers[J]. Sustainability, 2021, 13(16): 8737.
9	Chen Jing, Zhang Yong, Liu Lei. Vulnerability Analysis of Multimodal Transport Networks Based on Complex Network Theory[J]. Journal of Southeast University (English Edition), 2021, 37(2): 209-215.
10	Balac M, Hörl S. Simulation of Intermodal Shared Mobility in the San Francisco Bay Area Using MATSim[C]//2021 IEEE International Intelligent Transportation Systems Conference(ITSC). Piscataway, NJ, USA: IEEE, 2021: 3278-3283.
11	Chen X, Zhan F B. Agent-based Modelling and Simulation of Urban Evacuation: Relative Effectiveness of Simultaneous and Staged Evacuation Strategies[J]. Journal of the Operational Research Society, 2008, 59(1): 25-33.
12	Onelcin P, Mutlu M M, Alver Y. Evacuation Plan of an Industrial Zone: Case Study of a Chemical Accident in Aliaga, Turkey and the Comparison of Two Different Simulation Softwares[J]. Safety Science, 2013, 60: 123-130.
13	吴健宏, 翁文国, 倪顺江. 基于GIS和Multi-Agent的城市应急疏散[J]. 清华大学学报(自然科学版), 2010, 50(8): 1168-1172.
	Wu Jianhong, Weng Wenguo, Ni Shunjiang. Urban Emergency Evacuation Plans Based on GIS and Multi-agent Systems[J]. Journal of Tsinghua University(Science and Technology), 2010, 50(8): 1168-1172.
14	於家, 温家洪, 陈芸, 等. 基于应急疏散智能体模型模拟的城市避难所空间配置—以上海市静安区为例[J]. 地理学报, 2017, 72(8): 1458-1475.
	Yu Jia, Wen Jiahong, Chen Yun, et al. Spatial Configuration of Urban Shelters Based on Simulation Using Emergency Evacuation Agent-based Model: A Case Study in Jing'an District, Shanghai[J]. Acta Geographica Sinica, 2017, 72(8): 1458-1475.
15	Namoun A, TufailA, Mehandjiev N, et al. An Eco-friendly Multimodal Route Guidance System for Urban Areas Using Multi-agent Technology[J]. Applied Sciences, 2021, 11(5): 2057.

站点编号	范围	时间	站点名称
19	37	2 188	呼和浩特长途汽车东站
84	91	5 051	呼和浩特火车西站
89	92	4 602	呼和浩特火车东站
91	82	2 741	榆林火车站
98	79	2 387	靖边县火车站
100	55	3 462	清水河站

[1]	Yandong Liu, Gaoxiang Huang, Wen Chen. Improved Social Force Model Based on Enhancing Psych behavioral Heterogeneity [J]. Journal of System Simulation, 2023, 35(5): 1120-1130.
[2]	Guodong Yuan, Ming He, Ziyu Ma, Weishi Zhang, Xueda Liu, Wei Li. Multiagent Following Multileader Algorithm Based on K-means Clustering [J]. Journal of System Simulation, 2023, 35(3): 616-622.
[3]	Junren Luo, Wanpeng Zhang, Weilin Yuan, Zhenzhen Hu, Shaofei Chen, Jing Chen. Research on Opponent Modeling Framework for Multi-agent Game Confrontation [J]. Journal of System Simulation, 2022, 34(9): 1941-1955.
[4]	Hong Zheng, Zerun Liu, Jianhua Huang, Shihui Qian. Verification of Transaction Ordering Dependence Vulnerability of Smart Contract Based on CPN [J]. Journal of System Simulation, 2022, 34(7): 1629-1638.
[5]	Miaojia Lu, Chengyuan Huang, Jing Teng. Multi-agent Simulation for Online Fresh Food Autonomous Delivery [J]. Journal of System Simulation, 2022, 34(6): 1185-1195.
[6]	Lingjia Ni, Xiaoxia Huang, Hongga Li, Zibo Zhang. Research on Fire Emergency Evacuation Simulation Based on Cooperative Deep Reinforcement Learning [J]. Journal of System Simulation, 2022, 34(6): 1353-1366.
[7]	Xin Zhou, Weiping Wang, Yifan Zhu, Tao Wang, Tian Jing. An Unmanned Swarm Search Method Based on Human-Robot Cooperation [J]. Journal of System Simulation, 2022, 34(4): 735-744.
[8]	Feng Li, Ying Wei. Product Decisions in Presence of Social Learning and Reference Point Effect [J]. Journal of System Simulation, 2022, 34(2): 234-246.
[9]	Qiao Qiao, Yan Wang, Zhicheng Ji. Multi-robot Path Planning Based on CBS Algorithm [J]. Journal of System Simulation, 2022, 34(12): 2659-2669.
[10]	Qi Xiaolong, Yang Xuguang. Time-varying Output Formation Tracking Control of Discrete-time Heterogeneous Multi-agent Systems [J]. Journal of System Simulation, 2022, 34(1): 36-44.
[11]	Chengbing Li, Yunfei Li, Peng Wu. Study on Invulnerability of Urban Agglomeration Passenger Traffic Network Considering Time Characteristics [J]. Journal of System Simulation, 2022, 34(09): 2037-2045.
[12]	Cong Jiping, Cui Lijie, Ding Gang, Ren Bo. Simulation on Aviation Maintenance Support System Based on Goal-driven [J]. Journal of System Simulation, 2021, 33(9): 2157-2165.
[13]	Zhao Yu, Guo Jifeng, Yan Peng, Bai Chengchao. Self-learning-based Multiple Spacecraft Evasion Decision Making Simulation Under Sparse Reward Condition [J]. Journal of System Simulation, 2021, 33(8): 1766-1774.
[14]	Zhao Zhu, Wang Yi, Fan Ruifeng, Li Liya, Qi Meng. Modeling on Anti-UAV System-of-systems Combat OODA Loop Based on NetLogo [J]. Journal of System Simulation, 2021, 33(8): 1791-1800.
[15]	Gui Xindong, Ji Hongjiang, Fan Lingling, Liu Shida. Application of Trust Driven Adaptive Cooperative Control Algorithm [J]. Journal of System Simulation, 2021, 33(8): 1809-1817.