High-Resolution Mapping of Port Dynamics from Open-Access AIS Data in Tokyo Bay

TL;DR

This study demonstrates that open-access terrestrial AIS data can yield high-resolution (about 30 m) maps of port dynamics in Tokyo Bay, capturing vessel counts, transit rates, and berth usage over a 91-day window. By combining rigorous AIS data cleaning, a transit-area framework to handle intermittent reception, and a density-thresholding/ watershed approach to berth detection, the authors produce detailed spatiotemporal insights and identify 161 nearshore berths. They also infer receiver locations from AIS-shadow patterns, revealing privacy-sensitive information about urban data-collection infrastructure. The results align with official statistics and reveal a global trend toward fewer, larger vessels, while highlighting uncertainties from AIS non-use and data gaps that future SAR or auxiliary data could help resolve. The work showcases the value and privacy implications of open AIS data for port planning, safety, and economic analysis in dense urban coastal environments.

Abstract

Knowledge about vessel activity in port areas and around major industrial zones provides insights into economic trends, supports decision-making for shipping and port operators, and contributes to maritime safety. Vessel data from terrestrial receivers of the Automatic Identification System (AIS) have become increasingly openly available, and we demonstrate that such data can be used to infer port activities at high resolution and with precision comparable to official statistics. We analyze open-access AIS data from a three-month period in 2024 for Tokyo Bay, located in Japan's most densely populated urban region. Accounting for uneven data coverage, we reconstruct vessel activity in Tokyo Bay at $\sim\,$30~m resolution and identify 161 active berths across seven major port areas in the bay. During the analysis period, we find an average of $35\pm17_{\text{stat}}$ vessels moving within the bay at any given time, and $293\pm22_{\text{stat}}+65_{\text{syst}}-10_{\text{syst}}$ vessels entering or leaving the bay daily, with an average gross tonnage of $11{,}860^{+280}_{-\;\,50}$. These figures indicate an accelerating long-term trend toward fewer but larger vessels in Tokyo Bay's commercial traffic. Furthermore, we find that in dense urban environments, radio shadows in vessel AIS data can reveal the precise locations of inherently passive receiver stations.

Figures (17)

• Figure S1: Tokyo Bay region of interest (ROI) and positions of AIS-A messages received between 29 July and 27 October 2024. (a) Message frequency in the ROI. The panel covers longitudes $139.60^\circ\,\mathrm{E}$ to $140.15^\circ\,\mathrm{E}$ and latitudes $34.95^\circ\,\mathrm{N}$ to $35.70^\circ\,\mathrm{N}$. The gray-hatched area (lower part of the left panel) indicates the transit area of entering or leaving vessels. Additionally, three AIS receiver stations reported by the network are shown in red. The position of the Tokyo receiver with precision from AISHub AISHub2025 is shown as a red disk in the left panel, and the inferred position and the 68% containment range (CR) as a green ellipse (Section$\,$\ref{['sec:results_basestation']}). Average positions where first contact occurs with entering (black circle) and last contact with leaving (black diagonal cross) vessels are also shown, along with the zone where the JCG2020 estimates traffic through the Uraga Channel. (b) Close-up with message positions binned at higher resolution. Redder (brighter) colors indicate more messages per bin. The close-up illustrates radio shadows from signal occlusion by buildings ( Section \ref{['sec:methods_basestations']}) and circular patterns from vessels swaying around their anchor chains while moored Wijaya2024Visky2024Koizumi2015.
• Figure S2: Timeline of AIS-A message counts (top panel), inferred number of vessels operating in the ROI at each time (middle panel), and wind conditions in the bay (bottom panel) during the analysis period. The vertical dotted lines indicate the beginning of each Monday in local time (UTC+9). The black curves in the top and middle panels represent the sum of the other curves. The hatched gray-shaded intervals in the middle panel are excluded from the time-averaged estimates of the total number of vessels and of the stationary vessels in the ROI. Wind conditions are shown as the range of the initial values provided by the Copernicus2024 and GFS2024 forecast models, averaged over the ROI area. The passage of Typhoon Ampil along the Japanese coast on 16 August 2024 is indicated by the gray-shaded area in all three panels.
• Figure S3: AIS message rate (same as Figure \ref{['fig:ais_signals_time_tokyo']}, top) at different times of the day, as the average in 4-minute intervals over the 91-day analysis period. The times $\tilde{t}_\text{max}$ are the modes of maximum activity. The gap at 3:30 UTC is a data provider feature, and the peak in discarded messages at 4:44 UTC+9 is caused by isolated movements within the transit area (hatched area in Figure \ref{['fig:roi_data_map_tokyo_both']}a).
• Figure S5: Recorded journey of a liquefied petroleum gas (LPG) tanker (47,000 GT, 230m length). After the first AIS contact, the reported destination is Anegasaki port (JP$\,$ANE). Near Anegasaki, the signal is lost for 23h, after which the vessel proceeds to Kawasaki ME berth (JP$\,$KWS ME), anchors outside the harbor (JP$\,$KWS OFF), and finally departs toward Kinuura port (JP$\,$KNU) outside Tokyo Bay.
• Figure S6: Main and successive transit areas within the Tokyo Bay ROI. We use successive areas 1 to 8 by default (df). To assess systematic uncertainties, we define a hi (high) case using only successive areas 1 to 5. We add areas 9 and 10 to estimate the low case. For validation purposes, we also cut through Nakanose anchorage under label '3' (see Section \ref{['sec:discussion']}). We assume that receiver coverage decreases with distance from the Tokyo receiver (red disk). The message data from Figure \ref{['fig:roi_data_map_tokyo_both']}a are shown in the background. The extent of Figure \ref{['fig:lost_signal_example']} is indicated by the dashed frame.