内河船舶机会性互联的复杂网络模型及其时变特性

汪洋; 陈涛; 陈志强; 吴兵; 钟鸣

doi:10.3963/j.jssn.1674-4861.2024.02.003

内河船舶机会性互联的复杂网络模型及其时变特性

doi: 10.3963/j.jssn.1674-4861.2024.02.003

汪洋^{1, 2,},
陈涛^{1, 3, 4},
陈志强⁵,
吴兵^{1, 2},
钟鸣^{1, 2, ,}

1.
武汉理工大学智能交通系统研究中心武汉 430063
2.
水路交通控制全国重点实验室(武汉理工大学) 武汉 430063
3.
武汉理工大学交通与物流工程学院武汉 430063
4.
武汉理工大学三亚科教创新园海南三亚 572000
5.
长江水上交通监测与应急处置中心武汉 430014

基金项目:

湖北省自然科学基金项目 2021CFB324

国家自然科学基金项目 52372320

湖北省重点研发专项 2023BCB123

详细信息

作者简介:
汪洋(1976-), 博士, 副教授. 研究方向: 水上交通安全. E-mail: wangyang.itsc@whut.edu.cn

通讯作者:
钟鸣(1971-), 博士, 研究员. 研究方向: 交通信息工程及控制、交通安全等. E-mail: mzhong@whut.edu.cn

中图分类号: U491.5+4
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出版历程
- 收稿日期: 2023-10-30
- 网络出版日期: 2024-09-14

Complex Network Modeling and the Ephemeral Characteristics of Dynamic Opportunistic Interconnections Among Vessels in Inland Waterway

WANG Yang^{1, 2
,},
CHEN Tao^{1, 3, 4},
CHEN Zhiqiang⁵,
WU Bing^{1, 2},
ZHONG Ming^{1, 2
, ,}

1.
Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430063, China
2.
State Key Laboratory of Maritime Technology and Safety, Wuhan University of Technology, Wuhan 430063, China
3.
School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China
4.
Sanya Science and Education Innovation Park, Wuhan University of Technology, Sanya 572000, Hainan, China
5.
Yangtze River Waterway Transportation Monitoring and Emergency Center, Wuhan 430014, China)

摘要

摘要: 针对内河船舶间出现时空邻近的机会性现象开展建模及实证研究。在社会网络分析方法的基础上，提出了1种考虑时序特征的网络分析方法，将大尺度时间跨度上的网络聚类转化为小尺度跨度上的网络聚类，进而分析内河船舶在有限水域内的动态行为；考察船舶间形成邻近关系网络的时变特征，利用复杂网络表示船舶社会网络随时间的演化特性，并借助复杂网络模型对内河水域内存在较多互相熟识船舶的现象给出统计解释。基于长江下游200 km河段1个月的AIS数据，按时隙划分得到网络模型的序列，由此表现船舶间发生单跳数据交换关系的互联形态。实证结果表明：①船舶联网瞬时的度分布可用高斯分布拟合，拟合度在96%以上；②随着时间尺度的增加船舶社会网络的小世界特性和无标度特征愈加明显，网络形态在空间维度上呈现簇团情形，局部密集的组团网络由大部分静止和少量运动船舶连接起来，网络密度随时间缓慢增加至0.1左右，相对平均路径长度稳定在0.2~0.3之间，平均赋权集聚系数呈现缓慢下降的趋势最后趋于0.4~0.5，离散度较快趋向于1，并实现整体上的连通；③度值较高的船舶节点，其平均速度在不同时隙的船舶社会网络中分时段呈现相关性；④相对于船舶密度的增加，船舶在1 d内的平均友邻时间以指数形式递增，而船舶的重复相遇近似服从负指数分布。上述结果表明，内河船舶航行中数据交换关系的建立或断开是由物理空间中船舶间邻近关系的时变性决定的；内河船舶的历史交互行为对未来交互行为具有记忆性并产生影响。
- 交通安全 /
- 船联网 /
- 机会性互联 /
- 时序复杂网络模型 /
- 时变特性
Abstract: This paper empirically studies the opportunistic proximity among inland vessels. A social network analysis (SNA) method considering time-series characteristics is proposed based on the original SNA method, which transforms the network clustering with a large-scale time span into that with a small-scale span and could be used to analyze the dynamic behaviors of inland vessels in limited waters; additionally, considering the temporal characteristics of the proximity relationships among vessels, the complex network theory is employed to model the vessel social network (VSN), which explains the fact that many encountering ships are acquainted with each other in inland region. The AIS data from a 200-kilometer section of the lower Yangtze River in one month are used for demonstration. The results show that: ① the degree distribution of the VSN can be fitted with a Gaussian distribution with a fitting degree of over 96%; ② with the increase of time scale, small-world characteristics and scale-free features of the VSN become apparent, clusters sub-networks consisting of stationary vessels and sailing vessels are observed in the spatial dimension, the density of the VSN slowly increase to 0.1, the average path remains 0.2-0.3, the average weighted clustering coefficient slowly decreases and converges to 0.4-0.5, the dispersion rapidly approaches 1, and overall connectivity is achieved; ③ the average speed of the ships who have high degrees in the VSN with different time spans are highly correlated; ④ with the increase of vessel density, the average neighborhood time in 1 day grows exponentially and the repeated encounters fit a negative exponential distribution. In summary, the establishment or disconnection of data exchange relationships among sailing ships is determined by the ephemeral characteristics of the proximity relationships between vessels in physical space; the interaction behaviors of inland vessels have a memory effect on the interaction behaviors in the future, providing new insights for the research of inland traffic safety.
- traffic safety /
- internet of ships /
- opportunistic interconnections /
- time-series complex network model /
- ephemeral characteristics

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图 1 船舶间机会性互联的原理

Figure 1. Principles of opportunistic connections among ships

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图 2 芜湖-南京航段水域200 km AIS数据的提炼

Figure 2. Refinement of the 200 km AIS data in Wuhu-Nanjing sector waters

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图 3 芜湖-南京航段船舶交通流特征

Figure 3. Characteristics of ship traffic flow in Wuhu-Nanjing segment

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图 4 船舶社会网络的时空演变

Figure 4. Spatial and temporal evolution of ship social networks

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图 5 k =1，2，3时船联网度分布及各自高斯拟合

Figure 5. IoS degree distribution and respective Gaussian fits for k =1, 2, 3

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图 6 船舶社会网络1 h和1 d的度分布

Figure 6. Ship social network 1hour and 1day degree distribution

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图 7 船舶社会网络的空间结构

Figure 7. Layout of the spatial structure of the ship social network

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图 8 船舶社会网络参数的时变特征

Figure 8. Time-varying characteristics of ship social network parameters

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图 9 平均相遇船舶次数

Figure 9. Average number of encounters among ships

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图 10 船舶区域友邻时间网络

Figure 10. Ship area neighborhood time network

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图 11 船舶平均友邻时间与船舶密度关系

Figure 11. Average ship friendly neighbour time versus ship density

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图 12 船舶相遇次数分布

Figure 12. Distribution of ship encounters

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表 1 船联网节点度分布最优高斯拟合参数

Table 1. Optimal gaussian fitting parameters for node degree distributions in IoS

k	a	b	c	w	R²
1	0.005 76	0.092 78	5.678 06	3.614 14	0.974 97
2	0.004 78	0.091 25	5.516 48	3.708 41	0.969 76
3	0.004 55	0.092 32	6.083 05	3.787 92	0.964 58

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表 2 船舶社会网络的小世界特征

Table 2. The small-world character of the ship's social network

时间（月-日）	节点数N	边数E	C(k, k + 2879)	D(k, k + 2 879)	L(k, k + 2 879)	d(k, k + 2 879)
6-1	节点数N	边数E	C(k, k + 2879)	D(k, k + 2 879)	L(k, k + 2 879)	d(k, k + 2 879)	3 823	697 533	0.426	7	2.134	0.305
6-2	3 922	762 933	0.446	8	2.135	0.267
6-3	3 917	802 248	0.437	8	2.120	0.265
6-4	3 867	794 276	0.444	8	2.111	0.264
6-5	3 903	790 464	0.436	7	2.115	0.302

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表 3 船舶社会网络空间特征

Table 3. Spatial characterization of the ship's social network

网络图	节点数N	边数E	离散度γ	组团数	平均相遇船舶数	平均友邻时间/s
G_{(1, 1)}	665	2 917	0.426	43	8.77	28.92
G_{(1, 120)}	2 010	46 377	0.684	17	46.15	1 384.56
G_{(1, 2 880)}	3 823	697 533	0.896	3	364.92	10 947.61

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表 4 区域船舶友邻时间

Table 4. Regional ship friendly neighbour time

范围	重要节点（MMSI）	船舶节点i（MMSI）	船舶节点j（MMSI）	友邻时间/s		范围	重要节点（MMSI）	船舶节点i（MMSI）	船舶节点j（MMSI）	友邻时间/s
芜湖以下50 km	467 467 161	467 467 161	645 106 846	2 430		芜湖以下100 km	211 709 581	211 709 581	776 224 345	1 530
		467 467 161	834 228 004	1 020				211 709 581	458 063 163	1 170
		645 106 846	834 228 004	870				934 972 278	126 324 828	1 140
		829 904 595	671 039 974	540				318 252 935	368 229 080	1 110
		467 467 161	671 039 974	834 228 004	360				211 709 581	752 811 454	472 472 505	600
		393 359 141	207 419 604	120				502 713 373	819 418 141	150
		393 359 141	832 464 369	60				732 989 231	776 224 345	60
		207 419 604	798 296 303	30				298 476 590	458 063 163	30
		366 280 390	681 187 517	2 070				174 436 137	782 922 496	1 890
芜湖以下150 km	366 280 390	366 280 390	699 223 446	1 800		芜湖以下200 km	174 436 137	174 436 137	987 424 702	1 560
713 676 072	853 043 212	1 560		868 926 553	951 126 230	1 260
702 567 328	969 335 806	1 080		255 790 833	369 933 439	990
885 865 689	631 623 576	960		730 222 213	279 344 715	720
208 161 043	250 965 068	690		692 952 926	469 217 316	510
474 840 537	894 912 330	600		806 065 477	914 433 428	510
857 089 321	505 649 527	570		863 568 231	433 091 850	450
739 408 485	513 222 732	510		225 746 089	470 098 559	330
516 422 564	260 991 251	240		542 592 833	220 978 467	300
注^*：选取了2023年6月1日09:00—10:00芜湖至南京水域船舶社会网络部分节点。

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表 5 船舶节点运动模式

Table 5. Motion patterns of ship nodes

时间/s	节点度	节点个数	平均速度（/km/h）
30	0~10	464	6.686
	10~20	189	5.982
	20~30	52	1.352
	30~40	22	0.315
3 600	0~50	1 332	3.611
	50~100	629	3.815
	100~150	206	4.982
	150~200	16	10.279
	200~250	13	10.705
86 400	0~200	1 517	4.815
	200~400	731	6.093
	400~600	668	5.982
	600~800	537	5.575
	800~1 000	273	7.927
	1 000~1 200	92	8.130
	1 200~1 400	5	8.445

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