Jun 2026· International Conference on Mobile Data Management· pp. 319-322· 0 citations· 13 references
Abstract
Monitoring sea area use is essential for maritime safety, harbor management, and navigation planning. While the use of Automatic Identification System data has been studied extensively for trajectory-based tasks such as prediction, imputation, and collision prevention, such studies focus on individual vessel movements and do not provide area-level overviews of sea use—a key requirement for stakeholders such as harbor authorities and dredging agencies. We present the Seagull system for data-driven multi-level maritime traffic analysis. This system enables analyses of 16.4B AIS records from 101K vessels via a unified grid framework spanning from $40 \times 40 \text{km}$ cells, enabling regional traffic analyses, down to $2.1 \times 2.1 \mathrm{m}$ cells, enabling harbor-level analyses. By integrating with bathymetric depth models, the system supports safety-critical analyses involving under keel clearance, enabling identification of low-clearance zones and dredging needs. Employing a DuckDB star schema data warehouse for data storage, the system enables interactive analyses without pre-aggregation. Seagull is available online 11https://seagull.app.cs.aau.dk/.
Maritime navigation efficiency is commonly assessed using isolated route, speed, energy, or traffic indicators that do not fully represent voyage context. This study proposes a context-aware framework based on GPS–AIS data fusion, planned-route geofencing, metocean information, and encounter-based validation. The Navigation Efficiency Resilience Index (NERI) combines target achievement, trajectory-derived response activity, and disturbance intensity into a bounded, time-resolved diagnostic index. The framework was evaluated using a Singapore–Montevideo container-ship voyage with 30 s position data, surrounding-vessel AIS, corridor-specific cross-track limits, and collocated metocean variables. The voyage-level mean NERI was 0.679, and its 10th percentile was 0.519. Lower values occurred mainly in constrained waters, approach areas, and the metocean-intensive Cape transition, whereas the Indian Ocean and South Atlantic legs achieved higher mean values of 0.704 and 0.736, respectively. For the analysed datasets, the regular own-ship position record produced more stable trajectory-derived indicators than the less regularly sampled own-ship AIS series, without implying an inherent accuracy advantage. The full NERI formulation achieved an AUROC of 0.83 and an AUPRC of 0.41 for CPA/TCPA conflict-window classification. NERI therefore provides a decomposable, plan-relative analytical layer for retrospective voyage monitoring and diagnostics, but it is not a direct safety or collision-risk measure.
Y. Kalinichenko, Andrii Holovan, N.V. Vasalatii et al.· Future Transportation· 0 citations
Effective maritime anomaly detection is essential for ensuring navigational safety and safeguarding critical underwater infrastructure. In this paper, a supervised anomaly detection framework is proposed, which is tailored to the Baltic Sea, a region characterized by dense maritime traffic and recurrent submarine cable disruptions. The proposed approach uses Probabilistic Roadmaps (PRMs) to model feasible maritime routes, providing a spatial and contextual structure for feature extraction from Automatic Identification System (AIS) data. To address the lack of labeled anomalies, synthetic anomalous trajectories are generated using sinusoidal deviations, Markov-chain-driven movements, autoencoder-based transformations, and context-aware speed perturbations near submarine cables. Trajectory projections onto the PRM graph enable the extraction of geometric, behavioral, and context-aware features capturing navigational deviations and interactions with coastal and cable-rich regions. Feature selection is guided by Random Forest Importance (RFI) scores, with Recursive Feature Elimination (RFE) used to retain the most informative attributes. Optimized tree-based and ensemble classifiers support anomaly discrimination across cross-validation experiments on a balanced dataset of 858 real general cargo vessel trajectories and an equal number of synthetically generated anomalous ones. Tree-based models achieve validation ROC AUC scores above 0.81, with Random Forest reaching the highest validation accuracy of 73.2%. The soft-voting ensemble attains the highest validation precision (76.0%), favoring false-positive minimization in operationally sensitive settings. The framework is further evaluated on real-world trajectories associated with documented cable-severing incidents, illustrating its potential operational applicability while highlighting the need for broader external validation.
Gabriele Oliva, A. Tomei, Roberto Setola· IEEE Access· 0 citations
Maritime transport and port systems are facing major transformations driven by decarbonisation, vessel automation, and the growing digitalisation of navigation and port logistics. These changes are likely to affect shipping routes, service organisation, the expected skillset of workers, and port hierarchies within regional maritime systems. This paper presents a methodological framework to investigate possible maritime network transitions in the Ligurian Sea up to 2050 within the Interreg Marittimo JASON project. The study area is treated as a cross-border navigation system linking the French and Italian Riviera and Monaco, with strong interactions between dense traffic corridors, major and secondary ports, supervision systems, and environmentally sensitive zones such as PELAGOS. We intend to use Automatic Identification System (AIS) trajectories with complementary data on vessel characteristics, port infrastructure, weather and operational hazards, and environmental constraints. On this basis, General Morphological Analysis (GMA) will be used to structure a scenario space and to compare three tentative illustrative transition archetypes for 2050. The proposed framework is intended to specify the data used for the case study, and show how contrasting maritime futures can support the discussion of network evolution, port adaptation, and reskilling needs.
Clément Iphar, Eric Rigaud, J. Larouzée et al.· E3S Web of Conferences· 0 citations
In the context of multimodal-freight modeling efforts, the US Army Corps of Engineers (USACE), Engineer Research and Development Center (ERDC), Coastal and Hydraulics Laboratory (CHL), entered into an inter-agency agreement with the Department of Transportation (USDOT), Bureau of Transportation Statistics (BTS), to produce travel-time statistics for select portions of the US inland waterway transportation system. Statistics include the median, interquartile range average, and 10th–90th percentile averages between origin and destination (OD) sets selected based on vessel traffic activity, especially lock and dam locations, which are natural stopping points. Travel-time statistics are derived from position reports broadcast via Automatic Identification System (AIS) transceivers for vessel transits that occurred in 2021. This technical report summarizes the methodology and findings.
Marin M. Kress, Magdalena I. Asborno, Laura Dods et al.· 0 citations
Bottom-up ship emission inventories derived from Automatic Identification System (AIS) data are normally reported on kilometre-scale grids, which merge port waters that perform very different functions. Working with an AIS-based STEAM inventory for the Yangtze River Delta (YRD), we ask a question that gridded inventories rarely separate: are the cells where ships accumulate time the same cells where they emit? To answer it, we define the Static–Dynamic Ratio (SDR), the ratio of hotelling (auxiliary-engine) to propulsion (main-engine) emissions within a cell, and use it to classify YRD waters without recourse to external port charts. Hotelling-dominated cells occupy 17.7% of the sea area and accumulate 59.3% of all ship-hours, a ship-hour density seven times that of transit-dominated fairways, yet they carry only 22.4% of NOx. A vessel at anchor emits about one-eighth as much NOx per hour as one under way, and the two effects nearly cancel. The cancellation is species dependent: low-load correction factors are steeper for sulfur and particulate species than for NOx, so hotelling zones reach relative SO2 and PM2.5 densities of 1.21 and 1.09 against 0.89 for NOx. Coarsening the same activity field from 100 m to 1 km drops the share held by the busiest 1% of cells from 70% to 50%, showing how kilometre grids manufacture apparent continuity along shipping lanes. Activity hotspots are therefore not emission hotspots, and anchorage-targeted measures such as shore power are best justified by particulate and sulfur exposure near populated coasts rather than by their share of the regional NOx burden.
Chen Liu, Rongchang Chen, Shuting Sun et al.· Atmosphere· 0 citations
Railway corridors are often located in coastal areas, making them vulnerable to storm-induced failures. This study develops a framework to analyze performance of coastal rail infrastructure against storm-induced surge and waves with the consideration of climate change and sea-level rise. The procedure shifts from traditional scenario-based analysis to a fully probabilistic storm hazard model by using synthetic storm data sets. This probabilistic hazard model is integrated with fragility models for key failure modes, including ballast and embankment scour for inland tracks and deck uplift for ballast-deck concrete bridges. A sequential Monte Carlo simulation is then adopted to estimate life expectancies for rail track components considering long-term effects from climate change and sea-level rise. A case study is performed for rail tracks owned by CSX Transportation and Canadian National Railway spanning from Alabama to Louisiana. The results indicate potential vulnerabilities for approximately 18% of ballast-deck bridges and 34% of inland tracks. Furthermore, the effectiveness of various mitigation strategies, such as elevation and relocation of tracks and bridges, is investigated. This study provides a quantitative framework to estimate coastal railroad vulnerabilities, prioritize mitigation solutions, and devise retrofit actions.
Amir Tajik, S. Sohrabi, Y. Darestani et al.· Journal of Structural Engine...· 0 citations