内河船舶机会性互联的复杂网络模型及其时变特性  

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

作　　者：汪洋[1,2] 陈涛[1,3,4] 陈志强 吴兵[1,2] 钟鸣[1,2] WANG Yang;CHEN Tao;CHEN Zhiqiang;WU Bing;ZHONG Ming(Intelligent Transportation Systems Research Center,Wuhan University of Technology,Wuhan 430063,China;State Key Laboratory of Maritime Technology and Safety,Wuhan University of Technology,Wuhan 430063,China;School of Transportation and Logistics Engineering,Wuhan University of Technology,Wuhan 430063,China;Sanya Science and Education Innovation Park,Wuhan University of Technology,Sanya 572000,Hainan,China;Yangtze River Waterway Transportation Monitoring and Emergency Center,Wuhan 430014,China)

机构地区：[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

出　　处：《交通信息与安全》2024年第2期25-35,共11页Journal of Transport Information and Safety

基　　金：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)资助。

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。This paper empirically studies the opportunistic proximity among inland vessels.A social network analy-sis(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 characteris-tics of the proximity relationships among vessels,the complex network theory is employed to model the vessel so-cial 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 demonstra-tion.The results show that:①the degree distribution of the VSN can be fitted with a Gaussian distribution with a fitting degree of over 96%;②with 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;③the average speed of the ships who have high degrees in the VSN with different time spans are highly correlated;④with 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 establish-ment or disconnection of data exchange relationships among sailing ships is determined by the ephemeral character-istics 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 safe

关 键 词：交通安全 船联网 机会性互联 时序复杂网络模型 时变特性 

分 类 号：U491.54[交通运输工程—交通运输规划与管理]