Data-Driven Graph-Based Methods for In-Port AIS Vessel Trajectory Reconstruction

AIS trajectories in and around ports are often incomplete because of transmission interruptions, reception failures, and infrastructure outages. In this paper, we investigate whether the Data-driven AIS Trajectory Interpolation method (DAISTIN), which reconstructs missing trajectory segments using a graph derived from historical AIS observations, can be improved by retaining local directional information that is otherwise discarded when observations are sampled to form the graph. To this end, we introduce two extensions, xDAISTIN and xDAISTOUT. The originality of the proposed approach lies in extending DAISTIN by enriching each sampled graph node with neighbourhood-level directional information from nearby historical AIS observations. Specifically, local vessel headings are encoded cyclically and modelled using kernel density estimators, allowing candidate graph transitions to be evaluated using local movement probabilities rather than only the heading of the sampled point. The methods are evaluated on real-world AIS data from the Port of Antwerp by introducing synthetic gaps into held-out trajectories and measuring how well they are reconstructed by the methods. Using 36,748 moving sub-trajectories from 5488 vessel trips, we compare the proposed methods against DAISTIN, linear interpolation, and several directed, undirected, probabilistic, and shortest-path graph variants. Across graph sizes from 100 k to 180 k nodes, DAISTIN and its undirected variant reconstructed 48.48–84.61% and 72.35–91.23% of missing segments, respectively, compared to 88.02–96.42% and 91.36–99.13% for xDAISTIN and its variants, and 65.75–85.17% and 72.12–97.80% for xDAISTOUT and its variants. Compared with undirected DAISTIN at 180 k nodes, undirected xDAISTIN at 180 k nodes achieved significantly lower mean SPD, DFD, and HD (all Holm-adjusted, p<0.001), by approximately 14%, 43%, and 36%, respectively. These findings demonstrate that preserving neighbourhood-level directional behaviour during graph construction can substantially improve the interpolation completion rate and reconstruction accuracy in complex port environments.