Containerized Research Articles

Machine learning-based model for predicting arrival time of container ships

Forecasting container ship arrival times is challenging, requiring a thorough analysis for accuracy. This study investigates the effectiveness of machine learning (ML) techniques in maritime transportation. Using a dataset of 581 samples with 8 input variables and 1 output variable (arrival time), ML models are constructed. The Pearson correlation matrix reduces input variables to 7 key factors: freight forwarder, dispatch location, loading and discharge ports, post-discharge location, dispatch day of the week, and dispatch week. The ranking of ML performance for predicting the arrival time of container ships can be arranged in descending order as GB-PSO > XGB > RF > RF-PSO > GB > KNN > SVR. The best ML model, GB-PSO, demonstrates high accuracy in predicting the arrival time of container ships, with R2 = 0.7054, RMSE = 7.4081 days, MAE = 5.1891 days, and MAPE = 0.0993% for the testing dataset. This is a promising research outcome as it seems to be the first time that an approach involving the use of minimal and easily collectible input factors (such as freight forwarder, dispatch time and place, port of loading, post port of discharge, port of discharge) and the combination of a machine learning model has been introduced for predicting the arrival time of container ships.

Enhancement of Operational Safety in Marine Cargo Cranes on a Container Ship Through the Application of Authenticated Wi-Fi Based Wireless Data Transmission from Multiple Sensors.

The use of wireless technology in common marine engineering applications as a medium for data transaction in measurement and control systems, is not as popular as it should be. This article aims to demonstrate the advantages of using wireless technology in maritime engineering applications through a proposed Wi-Fi based wireless system dedicated to performance and safety monitoring in marine cargo cranes. The system is based on some concepts that were suggested in the earlier literature to perform authenticated data transmission from multiple sensors through using both the ESP-NOW protocol and the WebSerial remote serial monitor. The introduced system will be integrated with an already installed system in order to render the means for implementing effective principles in automation and control engineering, such as functional safety and predictive maintenance. Additionally, this article will highlight the economic efficiency of adopting wireless technology instead of cabling as a medium for data transaction in measurement and control systems in marine engineering applications such as cargo cranes.

Dynamic Flexible Allocation of Slots in Container Line Transport

Due to the imbalance between supply and demand, liner container transportation often faces the problem of low slot utilization, which will occur in the shipping process, such as dry container demand exceeding the available dry slots and reefer slots not being fully utilized. This makes it important and challenging to maintain a balance between the actual demand and the limited number of slots allocated for liner container transport. Therefore, this study proposes a flexible allocation method: expanding the types of containers that can be loaded in the same slot. This method is suitable for handling each dynamic arrival container booking request by shipping enterprises, making decisions to accept or reject, and flexibly allocating shipping slots. In order to maximize the total revenue generated by accepting container booking requests during the entire booking acceptance cycle, we establish a dynamic programming model for the flexible allocation of slots. For model solving, we use the Q-learning reinforcement learning algorithm. Compared with traditional heuristic algorithms, this algorithm can improve solving efficiency and facilitate decision-making at the operational level of shipping enterprises. In terms of model performance, examples of different scales are used for comparison and training; the results are compared with the model without flexible allocation, and it is proved that the model proposed in this paper can obtain higher returns than the model without flexible allocation. The results show that the model and Q-learning algorithm can help enterprises solve the problem of the flexible allocation of shipping slots, and thus, this research has practical significance.

Numerical analysis of coupling effect between sloshing and motion responses of a KCS in uni- and bi-directional waves

The seakeeping behaviour can be severely affected by the coupling between sloshing and ship motions in waves. The sloshing behaviour of liquid-loaded ships in bi-directional waves differs significantly from that in uni-directional waves. Given that ships operate in short-crested or multi-directional waves during their whole service period, it is significant to gain a deeper understanding of the effect of wave directionality on the coupling between sloshing and ship motions. In this study, a KRISO container ship (KCS) model with type-B liquefied natural gas (LNG) cargo tanks is adopted for coupling analysis. The 2D long-crested regular wave is adopted as an uni-directional wave, and three dimensional (3-D) cross wave is adopted as a bi-directional wave. The computational fluid dynamics (CFD) method is applied for the sloshing and seakeeping simulation of the ship model in different wave fields. The differences in wave elevation, sloshing forces, and coupling effects between ship motions and sloshing are studied in these two wave fields by adjusting control parameters such as ship heading angle and filling ratio. The comparative results indicate that sloshing behaviour and coupled motion responses in cross waves should be paid enough attention during ship design and evaluation.

Effects of Homogeneous and Inhomogeneous Roughness on the Ship Resistance

Ship hull roughness can significantly increase the ship resistance. The roughness caused by biofouling attached to the ship hull is not uniform but has a random distribution. The purpose of this study is to investigate how the inhomogeneous surface roughness distribution affects the ship resistance and the various resistance components. The KRISO container ship (KCS) is considered as a case study. To model the inhomogeneous surface roughness, the ship hull is divided into three segments with equal wetted surface area. Combinations of three roughness heights, denoted as P, Q, and R with ks values of 125 μm, 269 μm, and 425 μm, respectively, are considered to obtain homogeneous and inhomogeneous roughness arrangements (PPP, QQQ, RRR, PQR, PRQ, QPR, QRP, RPQ, and RQP). CFD method is utilized in this study, utilizing RANS equations and k-ω SST turbulence model. A VoF method is used to model the free surface. CFD simulation results show that for the homogeneous roughness, the total resistance coefficient CT increases with increasing ks (PPP < QQQ < RRR), as expected. For the inhomogeneous roughness, the friction resistance coefficient CF increases in the order PQR < PRQ < QPR < QRP < RPQ < RQP, consistent with results from earlier studies. In all the cases, the friction resistance (CF) is the dominant component of the total resistance. As Re increases from 2.2 x 109 to 2.7 x 109, the percentage of the friction resistance decreases, while the percentage of the wave resistance increases. The viscous-pressure resistance decreases slightly as Re increases from 2.2 x 109 to 2.7 x 109.

Cross-Pacific Vessel Estimated Time of Arrival and Next Destination Prediction with Automatic Identification System Data

With the increase in global trade uncertainty and supply chain disruptions, accurately predicting the estimated time of arrival (ETA) of container vessels can effectively help carriers, terminals, and freight forwarders improve operational efficiency. The Asia-North America route has been recently under stress because of strikes and trade wars between the U.S. and China. Voyages are subject to multiple external factors leading to uncertainty in arrival times. This is especially true for cross-Pacific voyages, where long distances without intermediate port visits allow for a large feasible set of trajectories and vessel speed profiles. Large errors in ETA prediction not only hinder the effective planning and execution of other stakeholders but also lead to significant fluctuations in the types and quantities of goods arriving at the port, thereby hindering port competitiveness and efficient multimodal transportation. Existing literature focuses on estimating ETA and next positions for dense, compact areas at the vicinity of ports. We propose and evaluate model framework based on artificial neural networks (ANN) fed by automatic identification system (AIS) historical data to predict the next destination and ETA for cross-Pacific routes for cases where ETA from the captain is missing in the AIS data. Results show our model can effectively predict next destination and ETA of vessels, achieving a mean absolute error value of 4 h when the vessel is 1,500 nmi away from the port. For comparison, the ANN submodules are replaced with gradient boosted trees, providing similar results. We terminate by highlighting the challenges found to improve the model.

Predicting practical ship powering and speed loss in waves using added resistance test results

ABSTRACT This paper introduces a practical technique for accurately estimating ship propulsion and speed loss in the presence of waves. Utilising the previously established self-propulsion estimation framework, our approach enhances the method, which previously solely considered bare hull resistance, propeller performance, thrust deduction factor, and wake fraction, by incorporating added resistance test results. The full-scale DTC container ship is the focus of our investigation, providing a thorough examination of its performance in different sea states. The model's predictive abilities are enhanced by incorporating test data on added ship resistance, and it addresses a crucial aspect that is generally disregarded while estimating self-propulsion. This research is considered to be a contribution to the advancement of ship hydrodynamics, as it offers a practical and reliable tool for ship designers, operators, and researchers to accurately assess powering requirements and speed loss in harsh ocean conditions.

Intermodal container terminal location and capacity design with decentralized flow estimation

This paper studies an intermodal container terminal (IMT) location and design problem, where the IMT operator wants to locate a set of open-access IMTs and design their capacity levels to maximize its profit. Following the IMT operator’s decisions, network users, responsible for container transportation, will independently choose their routes and may procure intermodal services from the IMT operator. Since only limited information is available before the existence of the network, we employ the entropy maximization principle as a least-biased approach to estimate the flow distribution resulting from the network users’ route choices. This enables the IMT operator to predict profit and evaluate the quality of its network design decisions. We formulate the problem as a mixed-integer bilevel nonlinear program, automatically embedding a decentralized flow estimation scheme into the optimization of IMT location and capacity design. By exploring the rationale behind the entropy maximization principle, our problem can also be interpreted as a leader–follower game, in which the IMT operator (as the leader) aims to maximize its profit and the network users (as the follower) maximize their welfare. Due to the bilevel structure and the nonlinear entropy function, the problem is extremely changeling. To support its application in real-world contexts, we propose both exact and approximation algorithms. Finally, we conduct a real-world case study on Sydney Greater Metropolitan Area and draw managerial implications.

Carbon footprints: Uncovering spatiotemporal dynamics of global container ship emissions during 2015–2021

The proposed “peak carbon” and “carbon neutral” targets have catapulted the reduction of carbon dioxide (CO2) emissions in the shipping industry into a global focal point. Given their pivotal role in global trade, the worldwide movements of container ships provide insights for decision-making in maritime shipping CO2 emission mitigation. This paper employs Automatic Identification System (AIS) trajectory data from 2015 to 2021 to estimate the global carbon emissions of container shipping through a bottom-up methodology, allocating emissions on a 1° × 1° grid. Additionally, it delves into the evolving trends in CO2 emissions from 2015 to 2021, conducts spatial autocorrelation analysis, identifies hot spots, examines the spatio-temporal distribution patterns of global container ship CO2 emissions, and analyzes the emission characteristics of major ocean shipping routes. The findings reveal a temporal pattern in global emissions from container shipping, characterized by an initial decrease followed by a rebound, notably declining from 217 million tons in 2016 to 148 million tons in 2020, and subsequently rising to 168 million tons in 2021. The spatial distribution of CO2 exhibits pronounced heterogeneity, marked by notable spatial dependence and aggregation tendencies. The spatial distribution of annual emissions from container shipping demonstrates a similar pattern, with peak emission clusters predominantly concentrated along the coastline and major ocean shipping routes. This study provides a comprehensive understanding of carbon emissions from container shipping with high spatial and temporal resolution, facilitating a detailed analysis of emission patterns and influencing factors. Furthermore, the outcomes offer a scientific foundation and decision-making support for environmentally sustainable practices in the global maritime shipping.

Container buildings used for residential and business purposes in Johannesburg, South Africa and potential heat-related health risks

Background Outdoor and indoor air temperature affects human health and wellbeing. Climate change projections suggest that global temperatures will continue to increase, and this poses a threat to health. Buildings (for housing and business purposes) that can protect humans from the adverse effects of temperature are essential, especially in the context of climate change. Method In this cross-sectional study, we measured the indoor temperature inside shipping containers comprising a seven-storey block of apartments and businesses in Johannesburg, South Africa for 14 days. We assessed indoor temperature and relative humidity; evaluated measured temperatures in relation to thresholds known to be associated with adverse health risks; and sought to understand heat-health perceptions and symptoms of people living and working in shipping container units. Results Median indoor apparent temperature (AT) (a combination of temperature and relative humidity) was 16°C with values ranging from 6°C (observed at 8:00) to 42°C (observed at 17:00). Insulated units had temperatures between 2°C and 9°C cooler than the uninsulated unit. Heat-health risks from AT exposure were likely in all units, although there was variation in the number of occurrences that AT measurements exceeded the four symptom bands of caution, extreme caution, danger and extreme danger. Indoor AT was found to be 7°C higher on average when compared to outdoor AT. Some participants believed that their units were hot during hot weather and most people opened windows or did nothing during hot weather. Few participants reported experiencing adverse heat-health impacts, except for experiencing headaches (58%) and feeling tired or weak (40%). Conclusion Residents, tenants, or business owners using shipping containers should consider insulation installation and adequate windows/air conditioning for ventilation, especially in hot climates. Further research and awareness regarding heat-health risks of living or working in these spaces is needed.

A Ship’s Maritime Critical Target Identification Method Based on Lightweight and Triple Attention Mechanisms

The ability to classify and recognize maritime targets based on visual images plays an important role in advancing ship intelligence and digitalization. The current target recognition algorithms for common maritime targets, such as buoys, reefs, other ships, and bridges of different colors, face challenges such as incomplete classification, low recognition accuracy, and a large number of model parameters. To address these issues, this paper proposes a novel maritime target recognition method called DTI-YOL (DualConv Triple Attention InnerEIOU-You Only Look Once). This method is based on a triple attention mechanism designed to enhance the model’s ability to classify and recognize buoys of different colors in the channel while also making the feature extraction network more lightweight. First, the lightweight double convolution kernel feature extraction layer is constructed using group convolution technology to replace the Conv structure of YOLOv9 (You Only Look Once Version 9), effectively reducing the number of parameters in the original model. Second, an improved three-branch structure is designed to capture cross-dimensional interactions of input image features. This structure forms a triple attention mechanism that accounts for the mutual dependencies between input channels and spatial positions, allowing for the calculation of attention weights for targets such as bridges, buoys, and other ships. Finally, InnerEIoU is used to replace CIoU to improve the loss function, thereby optimizing loss regression for targets with large scale differences. To verify the effectiveness of these algorithmic improvements, the DTI-YOLO algorithm was tested on a self-made dataset of 2300 ship navigation images. The experimental results show that the average accuracy of this method in identifying seven types of targets—including buoys, bridges, islands and reefs, container ships, bulk carriers, passenger ships, and other ships—reached 92.1%, with a 12% reduction in the number of parameters. This enhancement improves the model’s ability to recognize and distinguish different targets and buoy colors.

Developing a Container Ship Loading-Planning Program Using Reinforcement Learning

This study presents an optimized container-stowage plan using reinforcement learning to tackle the complex logistical challenges in maritime shipping. Traditional stowage-planning methods often rely on manual processes that account for factors like container weight, unloading order, and balance, which results in significant time and resource consumption. To address these inefficiencies, we developed a two-phase stowage plan: Phase 1 involves bay selection using a Proximal Policy Optimization (PPO) algorithm, while Phase 2 focuses on row and tier placement. The proposed model was evaluated against traditional methods, demonstrating that the PPO algorithm provides more efficient loading plans with faster convergence compared to Deep Q-Learning (DQN). Additionally, the model successfully minimized rehandling and maintained an even distribution of weight across the vessel, ensuring operational safety and stability. This approach shows great potential for enhancing stowage efficiency and can be applied to real-world shipping scenarios, improving productivity. Future work will aim to incorporate additional factors, such as container size, type, and cargo fragility, to further improve the robustness and adaptability of the stowage-planning system. By integrating these additional considerations, the system will become even more capable of handling the complexities of modern maritime logistics.

Investigation of some issues of automation of elements of terminal complexes for transshipment of large-capacity containers

The topic of the work is the study of the reasons for increasing the efficiency of the use of marine container transportation in the entire supply chain. It is noted that the increase in their efficiency is explained by the possibility of transporting various types of cargo, the speed of handling vehicles, as well as the compactness of storage and safety of cargo. The problem of increasing ship capacity on the terms of shipping companies, which currently exists and is being solved by container terminal operators, has been formulated, which allows minimizing the time of ships parking in the port for processing, which in turn can be achieved by increasing the capacity of ships. Attention is drawn to the fact that reducing costs by increasing the efficiency of lifting and transport equipment stimulates investments in automation systems for almost all terminal operations, and in particular in lifting and transport equipment systems. It is emphasized that the automation process of container terminals differs depending on the task at hand (a new project is being considered or work is being carried out to automate an existing terminal). The implementation of automation at existing terminals is a more difficult task due to the problems of compatibility with the types of activities performed and resistance to ongoing changes, which primarily involve systematic and repetitive technological processes that can be programmed. Attention is drawn to the fact that container terminals use the latest operating systems to control and optimize the movement of containers inside the warehouse area (in particular, they are used at terminals where gantry cranes on a pneumatic (RTG) and on a rail (RMG) are used), and it is also emphasized that when working with RTG technology gantry cranes on a pneumatic vehicle are equipped with sensors connected to satellite positioning systems, which are located on the terminal area and on wheeled carts and are brought to the place of cargo operation by signals from the control room, at the same time, the dock operator controls only when lowering or lifting the container. Such information systems are also used on terminals operating using RMG technology. It is noted that automation of berthing container cranes is currently under development, since the complexity of its implementation lies in the fact that during pitching the vessel has a roll and a deflector, therefore, when moving it, it is difficult to determine the exact location of the container and the alignment of the spreader horizon with the container horizon, as a result, the guidance process? It requires additional time, which leads to a decrease in work productivity.

Modeling and Optimization of the Inland Container Transportation Problem Considering Multi-Size Containers, Fuel Consumption, and Carbon Emissions

This paper investigates the inland container transportation problem with a focus on multi-size containers, fuel consumption, and carbon emissions. To reflect a more realistic situation, the depot’s initial inventory of empty containers is also taken into consideration. To linearly model the constraints imposed by the multiple container sizes and the limited number of empty containers, a novel graphical representation is presented for the problem. Based on the graphical representation, a mixed-integer programming model is presented to minimize the total transportation cost, which includes fixed, fuel, and carbon emission costs. To efficiently solve the model, a tailored branch-and-price algorithm is designed, which is enhanced by improvement schemes including a heuristic label-setting algorithm, decremental state-space relaxation, and the introduction of a high-quality upper bound. Results from a series of computational experiments on randomly generated instances demonstrate that (1) the proposed branch-and-price algorithm demonstrates a superior performance compared to the tabu search algorithm and the genetic algorithm; (2) each additional empty container in the depot reduces the total transportation cost by less than 1%, with a diminishing marginal effect; (3) the rational configuration of different types of trucks improves scheduling flexibility and reduces fuel and carbon emission costs as well as the overall transportation cost; and (4) extending customer time windows also contributes to lower the total transportation cost. These findings not only deepen the theoretical understanding of inland container transportation optimization but also provide valuable insights for logistics companies and policymakers to improve efficiency and implement more sustainable operational practices. Additionally, our research paves the way for future investigations into the integration of dynamic factors and emerging technologies in this field.

Vulnerability Evolution of a Container Shipping Network in an Uncertain Environment: The Case of China–United States Connections

Container transportation has the advantages of standardization, high efficiency, and high safety, which are essential for promoting the development of the world economy and trade. Emergencies such as severe weather, public health incidents, and social security incidents can negatively affect the operational reliability of the container shipping network. To ensure the network security and high-quality operation of container shipping, a double-layer coupled container transportation network is first described to analyze the evolution of the container shipping network and the risk propagation dynamics of operation participants. On this basis, a cascade failure model of the container shipping network considering risk level is constructed. To evaluate the vulnerability of the container shipping network, the transmission mechanism of cascade failure effects of the container shipping network under different emergency development trends and the evolution law and influence path of the container shipping network structure are both analyzed. Finally, we empirically studied the container shipping network in China and the United States, and characteristic parameters of the China–U.S. container shipping network are calculated and analyzed. The model’s validity is verified through practical cases and model simulation results, and the cascading failure process of the container shipping network in China and the United States under three types of attacks is simulated. Suggestions are provided for effective improvement in the vulnerability of the container shipping network under every kind of contingency.

Основные предвестники формирования общепринятых практик осуществления мультимодальных перевозок

The article provides an overview of the history of the emergence of containers, within the framework of such a phenomenon as the container revolution, and also analyzes the modern potential of large transport and logistics infrastructure facilities, as a result and result of the container development trend. As part of the review of the development of modern trends in multimodal transportation, the issue of containerization is quite important, because today a container is not only an equipment for cargo transportation, but also an integral part of the supply chain, and is also able to significantly influence the formation of market tariffs for cargo transportation. Containerization of cargo transportation contributed to the formation of a modern transport and logistics paradigm in the context of international transportation, and also influenced the evolution of existing types of vehicles and special equipment, adapting them to mass transportation of metal containers. Over the history of its existence and development, containers have been adapted for the transportation of a wide variety of types of cargo from dangerous and temperature to bulk and bulk. In this article, within the framework of the calculation unit for assessing the potential of the modern global transport and logistics system, an analysis of the capacity of the largest marine infrastructure facilities - seaports is used, since it was with sea transportation that the containerization process began.

Ship emission variations during the COVID-19 from global and continental perspectives

The COVID-19 pandemic and the International Maritime Organization's (IMO) 2020 fuel-switching policy have profoundly impacted global maritime activities, leading to unprecedented changes in shipping emissions. This study aimed to examine the effects from different scales and investigate the underlying drivers. The big data model Ship Emission Inventory Model (SEIM) was updated and applied to analyze the spatiotemporal pattern of global ship emissions as well as the main contributors in 2019 and 2020. Overall, ships emitted NOx, CO, HC, CO2, and N2O declined by 7.4 %–13.8 %, while SO2, PM2.5, and BC declined by 40.9 %–81.9 % in 2020 compared with 2019. The decline in CO2 emissions indicated a comparable reduction across vessel tonnages. Ship emissions occurring at cruising status accounted for over 90 % of the ship's CO2 emission reduction. Container ships, chemical tankers, and Ro-Ro vessels were the primary contributors to the emission reductions, with container ships alone responsible for 39.4 % of the CO2 decrease. The ship's CO2 emissions variations revealed the decline-rebound patterns in response to the pandemic. Asian-related routes saw emissions drop in February 2020, followed by a rebound in May, while European and American routes experienced declines starting in May, with a recovery in August. Further analysis of CO2 emission in Exclusive Economic Zones (EEZs) showed high temporal consistency between vessel CO2 emissions, sailing speeds, and international trade volumes across continents, and exhibited heterogeneity in main contributing ship type of emission reduction on continental scale. Our study reveals the short-term fluctuation characteristics of global ship emissions during the pandemic, particularly focusing on their spatiotemporal evolution and the inherent disparities. The results highlight the correlation between global ship emissions and trade, as well as the operational status of ships, and their rigidity.

Value-Focused Assessment of Container on Barge Freight Transportation Readiness

Container on Barge (COB) is a freight transport mode that utilizes barges to transport shipping containers. This mode has been relatively slow to develop in the United States compared to Europe and Asia. This research contributes a comprehensive and practical readiness assessment scorecard based on value-focused thinking for inland waterway ports to evaluate the feasibility of COB development. Based on a comprehensive literature review, the Container on Barge Readiness Assessment Scorecard covers important attributes and provides constructed measurements to assess COB port readiness. Applicability of the scorecard is demonstrated on nine global ports, and implications for engineering managers are presented.

The effects of tropical cyclone on the container shipping network: A case study of typhoon Ma-on (2022)

As climate change intensifies, tropical cyclones pose increasing challenges to container shipping networks (CSN). However, research related in this area remains insufficient. In this study, we analyzed the effects of Typhoon Ma-on on the CSN using over 776 million Automatic Identification System (AIS) trajectory signals. Our findings reveal that the typhoon not only altered the network structure of the CSN but also affected the importance of container ports, vessel flow, and travel distance fluctuations along shipping routes. After the typhoon, the recovery of the network’s connectivity and stability, as well as travel time fluctuations, was less robust compared to other aspects. Additionally, the impact ranges for port importance, vessel flow, and travel distance fluctuations were 1,750 km, 500 km, and 2,250 km, respectively, following an inverse distance decay pattern. These findings offer new insights for stakeholders to enhance the effectiveness of disaster prevention and mitigation policies.

Research on parametric roll of the polar environmental transport ship based on time-domain Rankine panel method

In this study, the roll damping of a polar environmental transport ship (PETS) is estimated using viscous CFD method, and the parametric roll is simulated based on time-domain Rankine panel method. This method's reliability is validated by contrasting the simulation results with the experimental data of C11 container ship. The roll damping of C11 container ship is estimated using CFD method and improved Ikeda's method, respectively. Then, the time-domain Rankine panel method is adopted to simulate the roll motions of C11 container ship, and the simulation results are contrasted with experimental data. It confirms that the roll damping is calculated by CFD method and the roll motions of ships are simulated based on time-domain Rankine panel method has good applicability. Finally, the influence of wave steepness, wavelength, and the ratio of encounter frequency ωe to natural roll frequency ωroll on the parametric roll of PETS are studied. It is found that the maximum roll angle of PETS increases along with wave steepness. As the wavelength increases, the possibility of parametric roll for PETS is also increasing. When the parametric roll of PETS occurs, the ωe/ωroll tends to increase.