Container Throughput Research Articles

A hybrid container throughput forecasting approach using bi-directional hinterland data of port

Accurate forecasting of port container throughput plays a crucial role in optimising port operations, resource allocation, supply chain management, etc. However, existing studies only focus on the impact of port hinterland economic development on container throughput, ignoring the impact of port foreland. This study proposed a container throughput forecasting model based on deep learning, which considers the impact of port hinterland and foreland on container throughput. Real-world experimental results showed that the proposed model with multiple data sources outperformed other forecasting methods, achieving significantly higher accuracy. The implications of this study are significant for port authorities, logistics companies, and policymakers.

Forecasting Container Throughput of Singapore Port Considering Various Exogenous Variables Based on SARIMAX Models

Forecasting Container Throughput of Singapore Port Considering Various Exogenous Variables Based on SARIMAX Models

En-route recharging scheduling strategy for electric trucks serving inter-terminal container transportation

ABSTRACT Container ports are increasingly deploying electric trucks (ETs) for inter-terminal transportation (ITT). However, en-route recharging activities, which reduce ITT efficiency, are necessary for ETs because of limited battery capacity. This study proposes a real-time strategy to schedule the en-route recharging activities for ETs. The strategy dynamically monitors the ITT system, and simultaneously designates charging piles for ETs and determines their positions in the service sequences. An ET may be designated to a remote but shorter-queued pile. The total queue time of the ETs served by a designated pile can be reduced by adjusting the service sequence. The effectiveness is demonstrated in comparison with an alternative strategy in a real-world case. The results indicate that the proposed strategy effectively reduces container throughput time by 21.8% and ET energy consumption by 12.3%. Sensitivity analyses suggest that the strategy is applicable for ports with multiple charging stations and moderate ET fleet size.

Research on Risks and Countermeasures of Berthing and Unberthing in Luojing Container Terminal

In order to further enhance the container throughput capacity of Shanghai Port, a total renovation of the terminal at Luojing port area is planned. The successful implementation of the renovation project not only needs to fully consider the updating of technology and facilities, but also requires an in-depth analysis and assessment of the risks of berthing and unberthing container ships. In this paper, we will collect and organize the hydrometeorological conditions around the terminal, as well as the existing conditions of the port area and the waterway. Through in-depth analysis of the traffic flow data of Luojing Container Terminal, this research will study the navigational environment in the vicinity of the terminal and explore the characteristics and changing trends of the traffic flow. These analyses will help to accurately assess the impact of traffic flow on the safety of berthing and unberthing of container ships. Eventually, this paper will combine the results of the traffic flow analysis of Luojing Container Terminal to propose corresponding risk assessment and response strategies for berthing and unberthing. These strategies will provide a scientific basis for the terminal management to make future decisions, ensure the smooth progress of the renovation project, and enhance the transportation efficiency and safety of the Shanghai port.

Container throughput forecasting of Nansha Port based on ARIMA-RBF

Container throughput forecasting of Nansha Port based on ARIMA-RBF

A Ternary Diagram Approach to Investigate the Competition within the Bohai Sea Rim Multi-Port Group

The Bohai Rim region constitutes the third prominent “growth pole” in China’s economic landscape, wherein the Bohai Rim multi-port system, encompassing Tianjin Port, Dalian Port, and Qingdao Port, engages in intense competition to establish itself as the foremost shipping hub in northern China. This study compares the ternary diagram method and employs the comprehensive concentration index (CCI), Lerner index (LI), and spatial shift-share analysis (SSSA) methods to delve into the intricacies of concentration, inequality, and evolving competitive dynamics within the Bohai Rim multi-port system over the four decades spanning from 1981 to 2023. The aim is to analyze the evolutionary trajectory and underlying dynamic mechanisms of this multipartite port system. The analysis delineates the development trajectory of the system into three stages: the dominant stage of Tianjin Port from 1981 to 1990, the efficiency competition stage from 1991 to 1996, and the ascendancy of Qingdao Port from 1997 to 2023. The results indicate that: (i) the Bohai Rim multi-port system exhibits a relatively low level of concentration, ensuring balanced growth within a non-monopolistic competitive environment; (ii) the internal competitiveness of the Bohai Rim multi-port system has gradually shifted from Tianjin Port to Qingdao Port, with Dalian Port experiencing steady development in its container transport capabilities. (iii) Dalian Port has witnessed a decline in container throughput since 2015, indicating a weakening competitive posture. These revelations suggest that Qingdao Port is a viable candidate for development into the northern China shipping center, leveraging its increasing competitiveness and strategic location. The method applied in this study may also prove beneficial for similar multi-port systems elsewhere.

The promotion of Shanghai’s position and function in the global container shipping network

ABSTRACT Shanghai’s container throughput has ranked first worldwide for 13 consecutive years beginning in 2010, indicating that Shanghai has built an international shipping centre capable of allocating resources for global shipping factors. This study considers global container shipping and shipping service networks as research objects to examine the node status and connection directions of Shanghai in these two networks. It also identifies the comparative advantages and weaknesses of Shanghai and proposes countermeasures and suggestions. The results show that Shanghai ranks high in the global container shipping network and is a sub-centre of the Busan system. Shanghai performs well in the mid-to-low-end shipping service industry in the shipping service network; however, a clear gap exists in the high-end service industry between Shanghai and European port cities. Thus, Shanghai should vigorously implement an international development strategy, actively promote a coordinated development strategy between ports, and integrate the construction of the shipping centre with that of financial and technological innovation centres.

Beyond throughput: Evaluating maritime port competitiveness using MABAC and Bayesian methods

The performance ranking framework of maritime ports conducted by the World Shipping Council relies solely on a singular criterion, such as container throughput. This ranking method lacks a comprehensive assessment of port competitiveness and may have biases. Therefore, this work proposes a new approach to measure the competitiveness of maritime ports by considering multiple performance criteria, such as port processing time, bunker fuel quality, bunker fuel suppliers, geographical advantages, and port security. To ensure fairness in weight estimation, both objective method (using the Entropy method) and subjective method (using the Best Worst method) are employed and integrated using Bayes’ theorem. Furthermore, the Multi-Attributive Border Approximation Area Comparison (MABAC) method is utilized to evaluate the ranking of ports based on the combined effect of these criteria, referred to as the Port Competitiveness Index (PCI). To test the effectiveness of the proposed methodology, the top 20 major maritime ports on the Asia-Europe service route are determined. This finding highlights the practical insights gained from the study, which can be applied to improve the performance of ports across different service routes worldwide.

A deep learning‐based multivariate decomposition and ensemble framework for container throughput forecasting

AbstractTraditional linear models struggle to capture the intricate relationship between dynamic container throughput and its complex interplay with economic fluctuations. This study introduces a novel, deep learning‐based multivariate framework for precision in demanding landscapes. The framework consistently outperforms eight established benchmark models by employing vital economic indicators like GDP and port tonnage, identified through rigorous predictor importance analysis of an initial set of four variables, including imports and exports. Statistical significance is demonstrably achieved through the Diebold–Mariano and Wilcoxon rank‐sum tests. Utilizing the Port of Singapore as a case study, the framework offers agile adaptability for the ever‐evolving global supply chain. Comprehensive analyses ensure robustness, decoding intricate throughput dynamics. Incorporating noise‐assisted multivariate empirical mode decomposition (NA‐MEMD) for nonlinear decomposition and bidirectional long short‐term memory (BiLSTM) for time series dependencies, this innovative approach holds promise for revolutionizing container throughput forecasting and enhancing competitiveness in the global market through optimized resource allocation and streamlined operations.

Forecast combination using grey relational analysis and Choquet fuzzy integral for container throughput forecasting

Utilizing the accurate and reliable demand forecasting model for the port logistics industry decision makers, can prevent misguided decisions ensuing from inaccurate forecasting. Current forecasting models of container throughput forecasting focus on the development of single or related hybrid models. However, these studies focus on improving the forecast accuracy, while neglecting the risk of failure. Combination forecasting can improve the forecast accuracy and reduce the risks from model selection failure. The previous research on port container throughput (PCT) combination forecasting focused on using a small number of linear models to generate the combined values, and neglected the nonlinear combined models reflecting the interaction between variables. Given the significance of combination forecasting for the PCT prediction, this study establishes a nonlinear combined model using the grey relational analysis (GRA) and Choquet fuzzy integral. The empirical results indicate that the proposed combined model outperforms both the other considered linear and nonlinear models. The findings assist the port decision-makers in both the public and private sectors for understanding the future PCT, and devising timely response plans aligned with actual circumstances and trends, besides formulating the appropriate development plans and decisions to modify the current situation or sustain competitive advantages.

Empirical analysis of the development relationship between Shanghai shipping industry and primary industry based on VAR model

This study employs the Vector Autoregression (VAR) model to conduct an in-depth empirical analysis of the dynamic relationship between Shanghai’s shipping industry and the development of the primary sector from 2002 to 2022. The findings reveal that, as a globally significant maritime hub, the development of Shanghai’s shipping industry has a notable positive impact on the growth of the primary sector, particularly in agriculture and fisheries. Through ADF unit root tests, cointegration tests, and Granger causality tests, a stable long-term relationship was identified between shipping industry indicators, such as container throughput and total waterborne cargo transport volume, and the output value of the primary sector. Impulse response analysis and variance decomposition further reveal the critical roles and mechanisms through which the shipping industry influences the development of the primary sector. Based on these insights, the paper proposes policy recommendations aimed at strengthening the integrated development of shipping and the primary sector, promoting technological innovation in the shipping industry, optimizing the policy environment, enhancing environmental protection measures, and bolstering international cooperation. These strategies are intended to foster synergistic development between the two sectors and drive sustainable economic growth.

Estimating the impact of container port throughput on employment: an analysis for African countries with seaports

Ports play a significant role in facilitating international trade and economic development, serving as vital gateways for the movement of goods across the continent and beyond. As global trade volumes continue to rise, efficient port operations hold the potential to not only enhance economic growth but also contribute significantly to job creation across various sectors of the economy. This paper examines the impact of container port throughput on employment in Africa and further tests whether causality runs from employment to container port throughput. To do so, we use a sample of 27 African countries with seaport and data spanning the period from 2010 to 2020 for the analysis. The system- Generalized Method of Moments (SGMM) estimation technique is used as the estimation strategy. We use service, industrial, and total employment percentages of the total population as proxies for employment while annual container throughput measured in Twenty foots Equivalent Units (TEUs) is used as an indicator for port throughput. Based on the empirical results, we establish a positive significant effect of port throughput on employment in Africa. We further show that bidirectional causality exists between port throughput and employment in Africa. Following these findings, we recommend policies that increase port throughput in Africa.

An empirical study of the performance of the sixth generation ports model with smart ports with reference to major container ports in mainland China

Since the UNCTAD proposed the initial port model, called the first generation port model (1GP) in 1994, its adaptation and transformation have evolved, reflecting not only the evolution of port operations and technological advancements, but also the resilience of ports to natural disasters and supply disruptions. Recently, the trio of COVID-19, digitalization, and decarbonization (CDD) has emerged as core challenges whilst providing numerous opportunities for building adaption and resilience for the port industry. In light of this, the sixth generation ports (6GP) model has emerged. This paper addresses crucial research gaps by empirically testing the 6GP model, emphasizing the impact of emerging factors of the CDD, including 1) the lack of empirical testing for the 6GP model and the emphasis on the CDD; 2) the exploration of factors contributing to the significant development of Chinese container ports; 3) the investigation into how the CDD lead to the reversal of the port devolution continuum path. In doing so, an empirical study has been conducted, focusing on six major container ports in mainland China, all of which are among the world’s top 10 container ports in terms of container throughput. It evaluates the importance of the criteria of the 6GP model with the performance of the six ports. The findings of this research are discussed for managerial and policy implications, enriching the existing literature on port devolution, and providing the port industry with a holistic view of port development and strategy in the post-pandemic period.

The effects of the COVID-19 pandemic on connectivity, operational efficiency, and resilience of major container ports in Southeast Asia

The COVID-19 pandemic in 2020 has had significant impacts on the connectivity and performance of container ports in Southeast Asia. The application of social network analysis (SNA) in conjunction with key indicators, such as container throughput, total capacity of ships, number of ship calls, and berth duration, provides a comprehensive understanding of the development of the regional port system. While major ports in the region, namely Singapore, Port Kelang, Manila, and Laem Chabang, have exhibited signs of recovery owing to the expeditious and timely implementation of targeted policies to mitigate the adverse effects of the pandemic, smaller ports in the region, such as Bangkok, Penang, Tanjung Priok, and Tanjung Perak, are grappling with enduring repercussions and encountering challenges in resilience. These challenges stem from inadequate infrastructure, limited accommodation capacity, low loading/unloading productivity, and other significant risk events associated with the pandemic, all of which have weakened their competitiveness. In contrast, ports in Vietnam, including Cai Mep, Hai Phong, and Ho Chi Minh, as well as Malaysia's Tanjung Pelepas, have demonstrated that they are less impacted by the COVID-19 pandemic. This can be attributed to the sustained growth of their respective economies and the continued surge in domestic production to meet the unabated demand for exports. Based on these findings, several policy recommendations can be suggested for port authorities to effectively manage and mitigate the impact of risky events, such as pandemics, in the future while also enhancing the competitiveness of ports.

A novel hybrid deep-learning framework for medium-term container throughput forecasting: an application to China’s Guangzhou, Qingdao and Shanghai hub ports

A novel hybrid deep-learning framework for medium-term container throughput forecasting: an application to China’s Guangzhou, Qingdao and Shanghai hub ports

Mathematical Programming-Driven Daily Berth Planning in Xiamen Port

In this paper, we introduce the daily berth planning problem for Xiamen Hai-Tian Container Terminal (XHCT) at the Port of Xiamen, China, and propose the development and implementation of a berth planning system. The aim of the berth planning problem is to optimize daily berth plans by considering various decisions, including berth allocation, quay crane assignment, fairway traffic control, and berthing safety requirements. Among these decisions, the berthing safety requirement is a novel but practical problem in berth allocation that concerns the resource allocation related to berthing safety and interrelation with other decisions. A mathematical programming-driven methodological framework is designed with a 0-1 integer linear programming model for problem formulation and a highly efficient decomposition heuristic algorithm for solving the problem. This framework establishes the core for the berth planning system. The adoption of the berth planning system contributes to the increase of container throughput and berth capacity by transforming the planning process of XHCT. Moreover, the mathematical programming-driven daily berth planning informs further intelligent operations development in the Port of Xiamen and other container ports. History: This paper was refereed. Funding: This research was supported by the National Natural Science Foundation of China [Grants 72394360, 72394362, 72025103, 71831008, 72361137001, 72071173, and 72371221].

Sustainable synergistic development of marine economic degree growth and marine art industry

Marine and port economy estimation is conducive to the understanding of the development law of marine economy degree. This study proposes a neural-learning network estimation model of the marine economy degree based on a priori architectural knowledge and adopts time-combined class array columns and multivariate modeling methods to estimate the indicators reflecting the development level of the marine economy degree in the ZJP region. The study adopts the PK_NN multivariate modeling method, taking cargo transportation volume, cargo turnover, cargo carrying value of ports near the sea, foreign trade throughput, port container throughput, and port container throughput as multivariate model inputs, and compared with the other modeling methods, the model of the time combination class array columns of type GM_11, which has a better comprehensive performance. Finally, the PK_NN time-combined class array column model is used to estimate the development level of the marine economy in the ZJP region near the seaport from 2011 to 2020, and the results show that the estimated value of the marine economy in the ZJP region is close to the actual planning value of the ZJP region. The algorithm was applied to estimate the economic degree curves of the five near-seaport areas of ABCDE under different harbor head construction art modes, and the results showed that the relative value error of the estimation was controlled between 4% and 10%, and the fluctuation ranges of each month's specific growth value area estimation were comparable. This proves the effectiveness and accuracy of the a priori marine neural-learning-based network algorithm in this paper.

Systemic Modeling and Prediction of Port Container Throughput Using Hybrid Link Analysis in Complex Networks

This paper introduces a hybrid framework for port container throughput forecasting, which is essential in global trade and transportation systems. It uses a multidisciplinary method that combines artificial intelligence, link prediction, and complex networks. To better grasp the interconnection and dynamics of port operations, time series data are first transformed using complex network theory into a network structure. The framework applies 13 similarity metrics, encompassing various aspects of network structural similarity, to form a feature set representing the complex port operation network. The most effective features are selected using the maximum relevance minimum redundancy (mRMR) method, adhering to systems theory’s efficiency principles. These features are processed through SVM, DNN, and LSTM models for link prediction, which is crucial for forecasting in port logistics. Finally, the methodology concludes with regression analysis to obtain container throughput forecasts, which is a key metric in port systems management. Case studies of Shanghai Port and Shenzhen Port validate the framework’s effectiveness, demonstrating a significant improvement in forecasting accuracy over the baseline models. This study contributes to systems analysis by showcasing a hybrid, AI-enhanced approach for managing and forecasting critical aspects of maritime trade systems.

Measuring economic convergence: evidence from container throughputs of ASEAN-5 countries

Measuring economic convergence: evidence from container throughputs of ASEAN-5 countries

Measuring economic convergence: evidence from container throughputs of ASEAN-5 countries

Measuring economic convergence: evidence from container throughputs of ASEAN-5 countries