Prediction of Arrival Time of Vessels Considering Future Weather Conditions

Abstract

International logistics is becoming increasingly active. Marine transportation, in particular, accounts for approximately 90% of the total volume managed in international logistics and plays a vital role in the supply chains of many companies. However, en route factors, such as weather conditions, often delay scheduled arrivals at destination ports, and an accurate prediction of the arrival time is required for supply chain efficiency. The arrival time has been predicted in previous studies by calculating the route to the destination port and the en route voyage speed without considering the influence of future weather conditions. Hence, the prediction accuracy may decrease when weather conditions change. In this study, we propose a prediction method that identifies the route from the voyage results of vessels whose weather condition is similar to the future one and uses Bayesian learning to calculate the voyage speed in consideration of future weather conditions. Consequently, future changes in weather conditions are reflected in the prediction results. The prediction accuracy of the proposed method is projected to be 28% higher than that from previous studies based on historical operational data of vessels carrying home appliance and automobile industry cargoes.

Long-Term Trajectory Prediction for Oil Tankers via Grid-Based Clustering

“…The availability of highaccuracy positioning data, together with other information such as speed and acceleration, makes it possible to predict the trajectories of the targets of interest automatically, which could find applications in many areas, including terrestrial navigation [11,12], autonomous driving [13,14], and maritime traffic management [2,8,15,16]. This paper focuses on the trajectory prediction problem for vessels, for which the existing works can be classified into two categories, i.e., short-term trajectory prediction [3][4][5][6][7] and long-term trajectory prediction [17,18].…”

“…In contrast, our work adopts DBSCAN to cluster the key points from many historical trajectories, where key points sharing similar characteristics are grouped into the same cluster. Two other relevant works on long-term vessel trajectory predictions can be found in [17,18]. Both these two works attempt to predict the remaining path of ships to a destination in order to better estimate the remaining traveling distance and to achieve a more accurate estimate of the arrival times.…”

“…Both these two works attempt to predict the remaining path of ships to a destination in order to better estimate the remaining traveling distance and to achieve a more accurate estimate of the arrival times. Ogura et al [17] evaluated the difference between the predicted weather and the weather of historical trajectories, and then adopts the historical trajectory with the smallest difference as the prediction. The AIS data of four cargos traveling to and from Japan was used to test the weather-based algorithm [17].…”

“…Ogura et al [17] evaluated the difference between the predicted weather and the weather of historical trajectories, and then adopts the historical trajectory with the smallest difference as the prediction. The AIS data of four cargos traveling to and from Japan was used to test the weather-based algorithm [17]. Alessandrini et al [18] adopted a graph-based approach by calculating the raster of density and directionality of all the historical data and applying a path-finding algorithm to identify a path from the current location to the destination.…”

A System to Improve Port Navigation Safety and Its Use in Italian Harbours

“…In fact, these phenomena influence the so-called harbour waterside management (optimization of maritime transport, dock performances, vessel mooring, logistics operations, ship loading, maritime works, marine water quality and pollution control). Therefore, their knowledge and forecasting can be very useful in order to minimize the risk of accidents (e.g., stranding of ships) and the consequent environmental impact and economic losses [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20].…”