Complex Network Modeling and the Ephemeral Characteristics of Dynamic Opportunistic Interconnections Among Vessels in Inland Waterway
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;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 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 safety.
traffic safetyinternet of shipsopportunistic interconnectionstime-series complex network modelephemeral characteristics
国家科技期刊平台
NSTL
万方数据
