The sustainable management of marine resources requires accurate knowledge of fishing activity patterns and their interaction with coastal infrastructure. Intelligent Transportation Systems (ITS) are increasingly applied in the maritime domain, where data-driven approaches enhance safety, efficiency, and sustainability. In this context, Automatic Identification System (AIS) data provide valuable insights into vessel behavior and fisheries management. This study employs the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to identify fishing grounds, and a density map-based approach to recognize port locations. By integrating AIS data with machine learning techniques, the study detects and analyzes fishing vessel activities, providing deeper insights into behaviors such as fishing ground visit times, durations, and transitions between fishing grounds and ports. A case study in the Aalesund area of Norway demonstrates that DBSCAN effectively reveals fishing activity patterns relevant to regulatory oversight and spatial planning, while density mapping accurately identifies fishing ports. The findings highlight the potential of AIS-based analytics and clustering methods within maritime ITS frameworks to enhance situational awareness, support compliance with fisheries regulations, and contribute to sustainable marine resource management. Using 2023 AIS data from the Aalesund region, 6 recurrent fishing grounds and 15 port locations are identified, and size-stratified visit frequency and residence-time distributions are quantified together with monthly seasonality in ground usage.

Automatic Identification System (AIS) data provide valuable insights into ship behavior, supporting maritime safety, situational awareness, and operational efficiency capabilities that are increasingly required for autonomous ship functions and harbor maneuvering assistance. This review synthesizes recent research on AIS-based ship behavior analysis and modeling published between 2022 and 2024 using a structured literature search and screening process informed by PRISMA principles. The review presents a five-stage workflow, spanning data processing, data analysis, knowledge extraction, modeling, and runtime applications with emphasis on how these stages contribute to perception, prediction, and decision support in automated navigation. Four dimensions are considered in data analysis, including statistical analysis, safety indicators, situational awareness, and anomaly detection. The modeling approaches are categorized into classification, regression, and optimization, highlighting current limitations such as data quality, algorithmic transparency, and real-time performance, while also assessing runtime feasibility for onboard or edge deployment. Three runtime application directions are identified: autonomous vessel functions, remote monitoring and control operations, and onboard decision-support tools, with numerous studies focusing on constrained waterways and port-approach scenarios. Future directions suggest integrating multi-source data and advancing machine learning models to improve robustness in complex traffic and harbor environments. By linking theoretical insights with practical onboard needs, this study provides guidance for developing intelligent, adaptive, and safety-enhancing maritime systems.

Background: Cruise and passenger vessel traffic is an important component of coastal transportation, but increasing activity can intensify logistical complexity, traffic interactions, and environmental pressure. Understanding vessel behavior across ports and sailing corridors is therefore necessary to support safer and more efficient transport planning. Methods: A reproducible AIS-based framework is proposed to integrate port- and route-level analysis. Port calls are detected using predefined port geofences and minimum dwell time criteria; operational states are classified from vessel location and speed; AIS-valid study-network voyages are reconstructed between consecutive qualifying calls; and modeled ${\mathrm{CO}}_2$ is estimated separately for hoteling and transit using representative engine power assumptions and speed-dependent load factors. The framework is applied to 2023–2024 AIS data from ten Norwegian ports. Results: The analysis identified 5546 port calls and 3323 AIS-valid voyages in 2023 and 5768 calls and 3794 voyages in 2024. Modeled hoteling ${\mathrm{CO}}_2$ decreased from 124,350 to 114,406 tons, while modeled transit ${\mathrm{CO}}_2$ increased from 1.027 to 1.076 million tons; transit ${\mathrm{CO}}_2$ per voyage decreased from 309.1 to 283.7 tons. Conclusions: Traffic frequency alone does not determine modeled emissions; integrated AIS analysis can support differentiated port- and corridor-level transportation planning and emissions mitigation.