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 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.