Introduction Effective maritime monitoring is essential for managing the complex nature of global trade routes. Out at sea, situations can shift rapidly and without warning. Weather changes, operator miscalculations, or equipment malfunctions can lead to major disruptions—from oil spills to major trade delays. A single blocked canal can disrupt supply chains across the globe, leaving maritime trade at a near standstill. With ships transporting about 80% of the global trade volume, any interruption in maritime passageways can cause delays lasting days or even weeks, leading to substantial financial losses across critical sectors such as food, manufacturing, and energy. Effective response requires immediate action—starting with adequate and up-to-date information not only on the congested passageway, but also on viable alternative routes. To address this, researchers from the Asian Development Bank (ADB) developed a novel computational framework for near real-time monitoring of maritime passageways. The method exclusively uses automatic identification system (AIS) data—high-frequency radio signals that report a vessel’s location at a given timepoint—to detect anomalous events and analyze their impacts on vessel routing behaviors. AIS-Based Real-Time Vessel Tracking Existing data sources are often limited by the timeliness or scope of their coverage. Given that maritime connectivity is a global affair that influences economies both large and small, there is a need for real-time methods to manage the unpredictability of shipping activities. While passageway analyses typically involve two points of interest, the new ADB method enhances this by going a step further–introducing a third data collection area to improve vessel tracking accuracy. Using the Suez Canal as an example, it used the North and South Anchorages as the end points while Great Bitter Lake serves as the midway point. Researchers focused on trade-related vessels and established AIS-based indicators of disruptions to maritime traffic flows, such as vessel counts, dwell and idle times, and rerouting incidents. Aligned with this, the computational framework consists of three major algorithms: transiting vessels identification, queued vessels identification, and rerouted vessel tracking. By establishing a baseline vessel transit time, the new method identifies queued vessels that exceed a threshold dwell time (transit and idle times). These higher-than-average dwell times indicate potential anomalous events may be unfolding, suggesting potential disruptions in the passageway. Given the broad range of rerouting options, researchers used a systematic approach to define points of interest for potential alternative paths. Several simulations were conducted to determine the most optimal spatial resolution to capture vessels transiting through these areas, ensuring accurate tracking without excessively driving up computational costs. The comprehensive framework supports traffic flow monitoring—tracking the number of vessels passing through a passageway and their transit times—and disruption impact analysis, which evaluates how congestions influence vessel rerouting. Raising Vessel Detection Accuracy To validate the framework, researchers used the March 2021 Suez Canal blockage as a case study. Perhaps one of the most infamous passageway disruption cases in modern times caused by a stuck ship, the blockage hampered global trade networks for several days. The incident provided a real-world scenario for testing the newly developed framework to investigate vessel voyages, including fluctuating transit and dwell times. The inclusion of a third point of interest improved vessel detection accuracy by an estimated 10% compared to other AIS-based detection methods upon validation against data from maritime authorities. The novel method also detected that close to three-quarters of all transiting vessels experienced significant delays in this period of obstruction, while others had opted to reroute. The average median dwell time skyrocketed for the waiting vessels, with a stark increase in idle time accounting for nearly 90% of the total dwell time. As congestion eased, this figure fell to 42%, signaling a return to normal maritime transport operations. Improving Vessel Routing and Disruption Management During the Suez Canal incident, vessels that were already in the vicinity of the obstructed area tended to wait. On the other hand, those that were en route to the canal but were still far from the area typically decided to change course and take alternative paths. Mapping these detours, researchers leveraged the framework’s disruption impact analysis and discovered that several vessels had rerouted following three distinct paths via the Cape of Good Hope—depending on their origins and destinations in the Americas, Asia, and Europe. Demonstrating that the new AIS-based method is generalizable, the researchers shifted their focus to other passageways and congestion incidents. The rerouting behaviors were different in partial disruptions, exemplified by geopolitical tensions restricting maritime travel along the Bosporus and Bab el-Mandeb Straits. Overall vessel counts were reduced in both cases, but unlike the Suez Canal incident where the route was completely blocked off, transit times and dwell times for these vessels remained stable. The ADB team’s framework illustrates how near real-time monitoring can be achieved even when using only AIS data, delivering a more comprehensive yet timely analysis of transiting, queued, and rerouting vessels in passageways of interest during anomalous events. This sets the method apart by being more scalable and adaptable to broader applications, compared to other AIS-based approaches that rely on combining various types of data for their analysis. In addition, the study showed that minimizing the impact of maritime disruptions will also require infrastructure developments, contingency planning, and dynamic identification of alternative routes, backed by timely information on the ever-changing conditions at sea. By delivering both accuracy and timeliness, the novel passageway monitoring framework serves as an important tool for stakeholders to develop more refined vessel routing and disruption response strategies. The computational framework can help maritime authorities, logistics companies, trade and environmental economists, and other researchers in proactive voyage management and informed decision-making. More broadly, this will effectively bolster the resilience of the global supply chain and enhance the industries that depend on maritime trade network and its continued transit flows. Resources C. Chico, et al. 2025. Analyzing Anomalous Events in Passageways with High-Frequency Ship Signals. PLoS One. 20 (4). Suez Canal Authority. 2024. Navigation Statistics. United Nations. 2025. United Nations Global Platform. United Nations Conference on Trade and Development. 2022. Review of Maritime Transport. Ask the Experts Cherryl Chico Consultant, Asian Development Bank Cherryl Chico is a consultant for data science and big data processing. Her work focuses on the use of big data, primarily on AIS data, as an alternative source of economic indicators. She earned her bachelor and master’s degree in applied mathematics at Ateneo de Manila University. Mac Cordel Statistics Officer (Data Science), Economic Research and Development Impact Department, Asian Development Bank Mac Cordel’s recent work leverages location intelligence, including AIS data for port efficiency and environmental monitoring, and mobile data for human mobility analysis, to address development challenges. These insights guide coastal infrastructure planning, urban-rural transition strategies, and tourism corridor development across emerging Asia. Mahinthan Joseph Mariasingham Principal Statistician, Economic Research and Development Impact Department, Asian Development Bank Joseph Mariasingham works in the Data Division and leads data development and statistical capacity-building initiatives in the System of National Accounts (SNA), global value chains, and statistical business registers. He started his career at Statistics Canada in 1999 and has specialized in SNA and input-output economics. He has considerable experience producing critical data and analysis for evidence-based policymaking. Follow Mahinthan Joseph Mariasingham on Elaine S. Tan Director, Economic Research and Development Impact Department, Asian Development Bank Elaine S. Tan serves as chief statistician and director of the Data Division. Her work includes building statistical capacity in developing member economies, undertaking economic research using new data sources and methods and providing support and policy advice to ADB operations. She holds a doctorate in economics from Cambridge University, UK. Asian Development Bank (ADB) The Asian Development Bank is a leading multilateral development bank supporting sustainable, inclusive, and resilient growth across Asia and the Pacific. Working with its members and partners to solve complex challenges together, ADB harnesses innovative financial tools and strategic partnerships to transform lives, build quality infrastructure, and safeguard our planet. Founded in 1966, ADB is owned by 69 members—49 from the region. Follow Asian Development Bank (ADB) on Leave your question or comment in the section below: View the discussion thread.