Search Results (3)

The present study proposes a new method that forecasts congestion in the area near a port by combining the automatic identification systems of ships and port management information data. The proposed method achieves 85% accuracy for one-day-long ship congestion forecasts. This accuracy level is high enough to act as a reference value for both manned and unmanned operation situations for autonomous vessels in port areas. The proposed forecast algorithm achieves 95% accuracy when used for a one-hour ship congestion forecast. However, the accuracy of the algorithm is degraded to almost half when the automatic identification system or the port management system is used independently. Full article

With the rapid development of global trade and maritime transportation, a comprehensive emission inventory is necessary to control air pollution from ships as well as to comply with international environmental standards. The purpose of this study is to calculate the volume of emissions from ships in Yeosu and Gwangyang ports in Korea based on a bottom-up approach and supported by the data from the automatic identification system (AIS) and Korean port management information system (Port-MIS). Specifically, a real set of data on specifications of all ships operating at the port in 2019 was collected and an analysis of ship movement using AIS data was conducted by the authors in this study to divide the route into cruising and maneuvering phases as well as to estimate engine’s load factor. Finally, results show that the total amount of air emissions from ships at the port area was 558 thousand tons, which is 96% from CO₂ and 66% from hoteling mode. Additionally, container and tanker ships together emitted the highest volume of air pollution accounting for a share of 73%. Full article

The purpose of this study is to prove the concept of the performance monitoring system of container terminals. PoC (Proof of Concept) is a realization of a certain method or idea in order to demonstrate its feasibility. The port authorities, such as government or local authority, are continually checking the performance of the terminals they invested in and want to reflect it in the development policy. They also want to increase competitiveness by checking performance levels, such as port handling volume, calling ships, resource utilization, and congestion. PPI (Port performance indicators) are classified into four categories: output (production), productivity, utilization, and service. In this study, 15 monitoring indicators for each stage by dividing the process from the ship’s entry to departure are defined. Four indicators, such as ship waiting ratio at anchorage, berth occupancy, storage occupancy, and truck turnaround time, are selected as PoC of monitoring platform. In addition, a method of collecting, processing, and expressing data on these four indicators in real time is presented. There are three steps to create PPI on monitoring platform. The information required for PPI is to be collected from the Port-MIS (Management Information System) and TOS (Terminal Operating System) databases. Second, the collected data from external entities are stored into the database after verification and classification. Third, descriptive PPI, predictive PPI are generated based on the input data. This study provides a 4-tier framework from the conceptual platform with the key elements of data presentation, data process and data interface and middleware. As a result of the study, it is proved to select monitoring indicators, define external entities, define internal elements of the system, develop systems, and present indicator results. However, in the process of collecting data outside the system, we have found there is confidential data of individual terminals. To this end, it is important to establish a mutual cooperation system for data collection. Full article