Paper Accepted to Ocean Engineering!!!

Accurate prediction of vessel estimated time of arrival (ETA) requires both reliable route reconstruction and efficient moving-time estimation. However, conventional maritime traffic networks (MTNs) often suffer from excessive network complexity and may inadequately represent heterogeneous vessel movements. This paper proposes a lightweight MTN (LMTN) framework that combines adaptive sampling, dual-feature spatial clustering of applications with noise (DF-SCAN), and two-stage sequential prediction (TSSP). Adaptive sampling selectively retains operationally informative automatic identification system (AIS) nodes, whereas DF-SCAN considers spatial proximity and navigational characteristics to construct representative route networks. TSSP incorporates voyage-level speed over ground information into segment-level moving-time estimation. Using largescale real-world AIS data, the proposed LMTN reduced network representation by 89.38% while maintaining competitive route-reconstruction performance and achieving zero route-generation failures in the evaluated trajectories. The average time required for route searching and ETA calculation was 0.064 s, compared to 1.519 s using dense H3 representation. ETA experiments showed that the LMTN-based prediction became more effective with increasing voyage distance; moreover, TSSP improved prediction accuracy under several mid- to long- range conditions. Experiments on various vessel types provided preliminary evidence of cross-vessel applicability, whereas supplementary deep-learning benchmarks provided further comparative context. Overall, the results demonstrate that lightweight route representation and hierarchical speed-informed moving-time estimation can support efficient vessel ETA prediction; moreover, the resulting route, speed, and ETA information can serve as supplementary decision-support inputs for vessel-arrival monitoring, berth planning, and terminal-resource coordination.