Maritime Activities Observed Through Open-Access Positioning Data: Moving and Stationary Vessels in the Baltic Sea

TL;DR

This study demonstrates that open-access AIS data can be transformed into reliable coastal maritime metrics for the Baltic Sea by implementing a rigorous cleansing, trajectory reconstruction, and uncertainty framework. The authors develop a full pipeline—from region definition and data acquisition to route simplification, speed modeling, and journey construction—that yields vessel counts, transit rates, and high-resolution density maps, along with inferred port areas. Their three-month analysis finds about 4,061 vessels in the Baltic ROI (roughly 77% stationary and 19% moving) and shows results consistent with proprietary data within 20%, validating open data for safety, environmental, and economic research. The work also provides a detailed uncertainty treatment for incomplete coverage, AIS usage, and data gaps, highlighting edge effects and suggesting avenues for enhancement and broader application.

Abstract

Understanding past and present maritime activity patterns is critical for navigation safety, environmental assessment, and commercial operations. An increasing number of services now openly provide positioning data from the Automatic Identification System (AIS) via ground-based receivers. We show that coastal vessel activity can be reconstructed from open access data with high accuracy, even with limited data quality and incomplete receiver coverage. For three months of open AIS data in the Baltic Sea from August to October 2024, we present (i) cleansing and reconstruction methods to improve the data quality, and (ii) a journey model that converts AIS message data into vessel counts, traffic estimates, and spatially resolved vessel density at a resolution of $\sim$400 m. Vessel counts are provided, along with their uncertainties, for both moving and stationary activity. Vessel density maps also enable the identification of port locations, and we infer the most crowded and busiest coastal areas in the Baltic Sea. We find that on average, $\gtrsim$4000 vessels simultaneously operate in the Baltic Sea, and more than 300 vessels enter or leave the area each day. Our results agree within 20\% with previous studies relying on proprietary data.

Figures (12)

• Figure S1: [15] Reported positions of the AIS-A messages received in the Baltic Sea ROI between 29 July and 27 October [25]2024. The figure spans from $9^\circ\,\mathrm{E}$ to $32^\circ\,\mathrm{E}$ in longitude, and from $53^\circ\,\mathrm{N}$ to $66^\circ\,\mathrm{N}$ in latitude. The 11 defined transit areas are also shown, along with the static receiver stations reported by the network. The orange dash-dotted line marks the longitude $\lambda=19.5^\circ\,\mathrm{E}$, used to assign messages to the UTC+1 (westwards) and UTC+2 (eastwards) time zones. All maps in this paper are shown in the Transverse Mercator projection.
• Figure S2: Timeline of received AIS-A message counts (top panel), inferred number of vessels operating in the ROI at the given time (middle panel), and prevalent wind conditions (bottom panel) during the analysis period. The vertical dotted lines denote the beginning of each Monday. The black curves in the top and middle panels represent the sum of the other curves. The gray-shaded three-week interval in the middle panel is used to derive time-averaged estimates of the total number of vessels and stationary vessels in the ROI (see \ref{['sec:vessel_metrics', 'sec:results']} for details). Wind conditions are shown as the range of the initial values given by the Copernicus2024 and GFS2024 forecast models averaged over the ROI area. Figures \ref{['fig:hist_times_messages_cart_baltic']} and \ref{['fig:hist_times_vessels_cart_baltic']} show the top and middle panels on a daily cycle.
• Figure S3: AIS message activity throughout the day, stacked over the 91-day analysis period (Figure \ref{['fig:ais_signals_time_baltic']}, top) using a bin width of 4min. The times $\hat{t}_\text{max}$ and $\tilde{t}_\text{max}$ denote the modes and circular means, and $\sigma_\text{c}$ the circular standard deviations Mardia1999. The sharp gap at 3:30 UTC is a data provider feature.
• Figure S5: Messages per vessel retained after the cleaning steps in Sections \ref{['subsec:static_removal']}--\ref{['subsec:duplicate_removal']}, sorted into logarithmic intervals based on the time differences between subsequent messages (a), distances (b), speeds (c), and accelerations (d) between those messages. The step in the acceleration histogram around $10^{-6}\,$m/□s is due to the static-report interval multiples and decimeter-length precision.
• Figure S6: An example of the reduction in original AIS messages (large green dots) into a simplified trajectory model. The model is defined by route waypoints (small black dots) and speed control points (violet dots) along the route. This example depicts a ferry trip from Umeå to Vaasa, departing on 2024/07/29 at 7:52:24 UTC+2 and arriving at 12:22:00 UTC+3. The AIS messages and speed points correspond to those in Figure \ref{['fig:speed_model_example']}, parametrized by time and distance.