Port Throughput Research Articles

Hierarchy and mobility of Latin America and Caribbean container ports

Research on port hierarchies within a maritime region is crucial for comprehending the dynamics of seaborne trade and the broader trends within the maritime sector and related supply chains. This study investigates the evolution of the Latin America and the Caribbean (LAC) container ports and regional sub-systems from 2000 to 2022, analyzing traffic dynamics, shifts in port throughput rankings, and market concentration trends in and among the different coastal regions. Conceptually, this analysis benefits from the notion of evolutionary phases, revealing the stages of a port system's development. Methodologically, 49 LAC ports of international interest are categorized into different coastal regions and tiers based on throughput sizes. Various metrics, including the Gini coefficient, Lorenz curve, Herfindahl-Hirschman Index (HHI), and Kendall Tau (τ), assess port hierarchy and related mobility since 2000. The observed shifts in the hierarchy and market structures, coupled with the fluctuating positions of critical ports, underscore the transition that LAC container ports dynamics experienced in the 21st century. Following a particularly dynamic and volatile evolution observed in the 2000s, the financial crisis of 2008/09 was a critical juncture leading to the transition of the LAC port dynamics from the ‘emerging’ to the ‘maturity’ phase. On the contrary, the pandemic (2019) has not affected the pre-existing hierarchy or the moderate market concentration that was already in progress. The LAC region comprises port sub-systems (coastal ranges) subjected to different tendencies towards (de)concentration, yet mobility in port rankings within each sub-system decreases. The study also reveals the coexistence of this transition with a structural transformation of port governance structures, i.e., granting of concessions of operations to third parties. In 20 of these ports and the above-average mobility of ports are operated by Global Terminal Operators (GTOs), and calls for further research on the (non) causality of these phenomena.

An Integrated Framework for Estimating Origins and Destinations of Multimodal Multi-Commodity Import and Export Flows Using Multisource Data

Estimating origin-destination (OD) demand is integral to urban, regional, and national freight transportation planning and modeling systems. However, in developing countries, existing studies reveal significant inconsistencies between OD estimates for domestic and import/export commodities derived from interregional input-output (IO) tables and those from regional IO tables. These discrepancies create a significant challenge for properly forecasting the freight demand of regional/interregional multimodal transportation networks. To this end, this study proposes a novel integrated framework for estimating regional and international (import/export) OD freight flows for a set of key commodities that dominate long-distance transportation. The framework leverages multisource data and follows a three-step process. First, a spatial economic model, PECAS activity allocation, is developed to estimate freight OD demand within a specific region. Second, the international (import and export) freight OD is estimated from different zones to foreign countries, including major import and export nodes such as international seaports, using a gravity model with the zone-pair friction obtained from a multimodal transportation model. Third, the OD matrices are converted from monetary value to tonnage and assigned to the multimodal transportation super network using the incremental freight assignment method. The model is calibrated using traffic counts of the highways, railways, and port throughput data. The proposed framework is tested through a case study of the Province of Jiangxi, which is crucial for forecasting freight demand before the planning, design, and operation of the Ganyue Canal. The predictive analytics of the proposed framework demonstrated high validity, where the goodness-of-fit (R2) between the observed and estimated freight flows on specific links for each of the three transport modes was higher than 0.9. This indirectly confirms the efficacy of the model in predicting freight OD demands. The proposed framework is adaptable to other regions and aids practitioners in providing a comprehensive tool for informed decision-making in freight demand modeling.

제미나이 출범에 따른 글로벌 주요 항만의 영향 분석

Purpose : The purpose of this study is to analyze the representative differences between the services implemented by the launch of the Gemini alliance and the existing alliance services. Additionally, it forecasts the impact of the Gemini launch on Busan Port and other top 20 global ports. Research design, data and methodology : An empirical analysis is conducted using data from the Gemini alliance and the top 20 global ports. Specifically, the analysis derives the impact on ports by utilizing the representative differences of the Gemini service, such as the size of the ships and the frequency of regular service calls, including GDP. Results : All three independent variables (ship size, frequency of regular service calls, and GDP) were found to have a significant impact on port throughput. In particular, the size of the ships and the frequency of regular service calls have a relatively greater impact compared to GDP. Conclusions : The frequency of regular service calls by Gemini at Busan Port is expected to decrease significantly, raising concerns about a potential reduction in Busan Port's throughput. However, considering the increase in the size of feeder ships and Gemini's market share of 20%, it is deemed unlikely that Busan Port's throughput will immediately decrease significantly due to the Gemini launch. Therefore, the success of the differentiated services promoted by Gemini is crucial.

Port Throughput Forecast Based on ARIMA Model-Take Tianjin Port as an Example

Port Throughput Forecast Based on ARIMA Model-Take Tianjin Port as an Example

Are vessel arrival and port operations affected by COVID-19? Evidence from the Hong Kong port

Efficient vessel arrivals and departures are crucial for the smooth functioning of port operations and global supply chains. Nonetheless, the emergence of the Coronavirus disease (COVID-19) at the end of 2019 has presented unprecedented challenges to the shipping industry worldwide, disrupting vessel schedules and operations due to measures aimed at minimizing in-person interactions and restricting the movement of goods and people. The influence of the COVID-19 on vessel arrivals at ports around the world has been significant and far-reaching, with many ports struggling to maintain their efficiency and effectiveness. This study takes the Hong Kong port (HKP) as an example to evaluate the impact of the COVID-19 pandemic on vessel arrivals and port operations. Data from 2019 to 2022 on vessel arrivals and port operations are collected to examine whether, how, and why the pandemic has affected these arrivals and the related port operations. Specifically, we use four general indicators, namely the number of vessel calls, vessel total net tonnage (NT), vessel average gross tonnage (GT), and port total throughput, to comparatively evaluate the COVID-19 impact on vessel arrival. Additionally, we quantitatively assess the effects of COVID-19 on vessel arrival punctuality and vessel turnaround time at the HKP. Our analysis reveals that the COVID-19 and associated restrictions have led to fewer vessel calls across various vessel types, a decrease in overall port throughput, and consequently, reductions in total vessel NT and average GT. Furthermore, the pandemic-related restrictions have exacerbated vessel arrival delays and increased vessel turnaround time during the pandemic, further reducing the efficiency of port operations. To address these challenges, our study also proposes policy recommendations and management guidance for the port to enhance its resilience and efficiency in the post COVID-19 pandemic era. By shedding light on effects of the COVID-19 pandemic on vessel arrival and port operations, our study contributes to a deeper understanding of the challenges faced by the shipping industry and provides insights and recommendations for the future development of port operations in the post COVID-19 era.

Optimizing berth-crane allocation considering tidal effects using chaotic quantum whale optimization algorithm

With the normalization of the global epidemic, shipping has gradually resumed, resulting in a surge in port throughput. Terminal managers are having great difficulty in making container ports be stable, orderly, and efficient. For small and medium-sized ports, making full use of the tidal water level to increase the operation time of large ships significantly improves the efficiency of port operations. Therefore, considering the impact of tidal factors on the operation of container ports, this paper firstly proposes a new berths and quay cranes allocation optimization model, T-B&QC, that minimizes the distance between the actual berthing berth and the preferred berth, the port time cost of ships and the number of quay crane movements as the optimization objectives, under constraints that consider tidal factors. Then, to solve the T-B&QC model using chaotic mapping and quantum theory, the Whale Optimization Algorithm (WOA) is integrated by using the Chaotic Quantum Rotating Gate Algorithm (CQRGA) and the Quantum Not Gate Algorithm (QNGA), whale coding rules are designed, and the Feasible-Integer Processing Algorithm (WF-IP) is established. Afterwards, the Chaotic Quantum Whale Optimization Algorithm, CQWOA, is proposed. Finally, the CQWOA is used to develop a new berth and quay crane allocation optimization method, T-B&QC_CQWOA. Subsequently, data from an actual seaport container port is used to test the reliability and superiority of the proposed distributing approach and the established optimizing algorithm. Numerical results demonstrate that the proposed distributing approach outperforms the classical distribution models that are selected herein, and the CQWOA yields a coordinated schedule of higher quality than the others in the process of solving the model.

Autonomous navigation and collision prediction of port channel based on computer vision and lidar.

This study aims to enhance the safety and efficiency of port navigation by reducing ship collision accidents, minimizing environmental risks, and optimizing waterways to increase port throughput. Initially, a three-dimensional map of the port's waterway, including data on water depth, rocks, and obstacles, is generated through laser radar scanning. Visual perception technology is adopted to process and identify the data for environmental awareness. Single Shot MultiBox Detector (SSD) is utilized to position ships and obstacles, while point cloud data create a comprehensive three-dimensional map. In order to improve the optimal navigation approach of the Rapidly-Exploring Random Tree (RRT), an artificial potential field method is employed. Additionally, the collision prediction model utilizes K-Means clustering to enhance the Faster R-CNN algorithm for predicting the paths of other ships and obstacles. The results indicate that the RRT enhanced by the artificial potential field method reduces the average path length (from 500 to 430m), average time consumption (from 30 to 22s), and maximum collision risk (from 15 to 8%). Moreover, the accuracy, recall rate, and F1 score of the K-Means + Faster R-CNN collision prediction model reach 92%, 88%, and 90%, respectively, outperforming other models. Overall, these findings underscore the substantial advantages of the proposed enhanced algorithm in autonomous navigation and collision prediction in port waterways.

An integrated intermodal freight transportation system to avoid container supply chain disruptions in Chattogram Port of Bangladesh

An integrated intermodal freight transportation system is a derived demand to transport shipping containers from/to the seaport to/from the hinterland, instead of undertaking cargo loading and unloading activities in port-protected areas. The concept of the intermodal system is to transfer containers to nearby hinterland nodes, such as dry ports, inland rail, road, and river terminals as a part of port development, as well as increasing port efficiency and productivity. Lloyd’s List ranked Chattogram Port as the 67th busiest container port in the world as per container port throughput in 2022. Chattogram Port has small-scale intermodal facilities for off-docks/ICDs in the port city only. In addition, a pure intermodal system has been developed by one rail ICD in the capital city, Dhaka, and two RICTs nearby Dhaka, but less than 5 % of port throughput. A large-scale intermodal for serving the major cities, SEZs, EPZs, and industrial areas is absent due to planning and policy problems, as well as the unavailability of intermodal infrastructure and network. Due to an improper hinterland intermodal chain, the port faced container supply chain disruptions at the port yard and exceeded port capacity, resulting in container vessel congestion at the outer anchorage of Chattogram Port. By developing rail and road infrastructure, and utilizing natural river connectivity, it is possible to develop road, rail, and river intermodal systems, and shape an integrated intermodal freight transportation system to avoid container supply chain disruptions in Chattogram Port. To develop an integrated intermodal system, this research chose a quantitative research methodology for port throughput data analysis and forecasting future throughputs that are advanced to the intermodal model for Chattogram Port. Overall, this paper aims to develop an integrated intermodal freight transportation system to support Chattogram Port to keep and increase the record of port productivity, efficiency, and competitiveness actively. Highlights Intermodal system is a derived demand for managing hinterland and port terminals of a seaport that needs to be implemented in Chattogram Port Container supply chain disruptions to be avoided to increase the port productivity and efficiency Inland intermodal connectivity will help to increase hinterland of a seaport It is necessary to develop an integrated intermodal freight transportation system to support Chattogram Port to keep and increase the record of port productivity, efficiency, and competitiveness globally

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.

Analysis of logistics capacity, influencing factors and spatial spillover effect in Yangtze River Economic Belt.

The logistics industry plays a crucial role in facilitating regional economic development. Serving as a vital link connecting producers, consumers, and various components of the supply chain, it has a direct and profound impact on the prosperity and advancement of regional economies. Based on the panel data of 11 provinces in the Yangtze River Economic Belt from 2016 to 2020, this paper constructs the logistics capability evaluation index system from four aspects: regional economic base, logistics infrastructure, logistics development scale, information technology and talent support, and uses the entropy weight TOPSIS method to measure the logistics capability of each province. The adjacency space weight matrix, geographical distance weight matrix and economic distance weight matrix are selected to build a spatial econometric model to analyze the influencing factors and spatial spillover effects of regional logistics capability. The following conclusions can be drawn from the analysis. From 2016 to 2020, the regional logistics capacity of the Yangtze River Economic Belt shows a trend of increasing year by year, but the logistics capacity of different provinces within the region has a large room for improvement. From the perspective of spatial dimension, the logistics capacity of the Yangtze River Economic Belt is "high in the east and low in the west". The results of spatial econometric analysis based on the spatial Durbin model show that there are significant spatial spillover effects on the logistics capacity of provinces in the Yangtze River Economic Belt. Factors such as road network density, port throughput, water freight turnover, transportation, warehousing and postal employment will not only affect the logistics capacity of the region, but also have a spillover effect on the material capacity of neighboring provinces in the Yangtze River Economic Belt. This study improves the level of regional logistics capacity and promotes the regional economic development of the Yangtze River Economic Belt. It can be used as a reference for other regions or countries in terms of enhancing regional logistics capacity and promoting regional economic development.

Active stabilisation of seaports co-evolution system for port throughput with time delay

Active stabilisation of seaports co-evolution system for port throughput with time delay

Mixed-frequency data-driven forecasting port throughput: A novel attention-DeepAR-MIDAS model

Mixed-frequency data-driven forecasting port throughput: A novel attention-DeepAR-MIDAS model

Neural network-based prediction system for port throughput: A case study of Ningbo-Zhoushan Port

Port throughput is a crucial indicator for assessing the efficiency of port enterprises. Accurately predicting long-term port throughput can help mitigate the risk of investing in major infrastructure during the development process. This study introduces a neural network-based system for predicting port throughput at the Ningbo-Zhoushan Port. By utilizing the grey relational analysis method, we identified 12 highly correlated factors that affect port throughput variation in Ningbo-Zhoushan Port. These factors were then used as input indicators for the BiLSTM model to predict port throughput. Our research indicates that the proposed model achieved a high level of accuracy, with a MAPE value of 2.95%, outperforming commonly used machine learning and neural network models. Additionally, the model's predictions suggest a rapid growth trend in cargo throughput at Ningbo-Zhoushan Port by 2026. Consequently, we provide relevant suggestions to address this trend. Our research can assist port enterprises in making informed decisions, contributing to the field of port logistics.

Spatiotemporal distribution characteristics of sea ice disasters in the Northern China Sea from 2001 to 2020

The sea ice disaster is one of the major marine disasters in China. The presence of sea ice poses a potential threat to the social-environmental system in the Northern China Sea during winter. The study focused on investigating the spatiotemporal distribution characteristics of sea ice disasters in the Northern China Sea during 2001–2020, including exploring the variation of sea ice hazards, climate drivers influencing sea ice occurrence, and the subsequent impacts on maritime activity and marine aquaculture. The results show that the sea ice grade in the Bohai and Northern Yellow Seas (BNYS) exhibited a slight downward trend (−0.40/decade, P > 0.10) during the past two decades. The winter temperature and the frequency of cold waves are the primary local climate factors for sea ice variation. The decrease in sea ice grade in the BNYS due to global climate change does not eliminate the occurrence of extreme events. However, the potential for more severe damages remains. It is worth noting that the winters of 2010, 2011, and 2013 stand out as periods with the highest recorded direct economic losses caused by sea ice hazards. In terms of spatial distribution, Liaoning and Shandong provinces have experienced the most severe impacts of sea ice hazards. Sea ice has a negative impact on the throughput of ports that are influenced by sea ice. Significant moderate negative correlations (r = −0.40, P < 0.10) have been observed between port throughput and the sea ice index in Yingkou, Jinzhou, and Tianjin ports. Moreover, the mariculture industry emerged as the primary sector affected by sea ice disasters in the Northern China Sea between 2001 and 2020. Attention should be paid to Haizhou Bay shortly as marine aquaculture in this area has been seriously affected by sea ice in the past five years. In the future, it is crucial to prioritize more accurate and refined monitoring and forecasting of sea ice, enhance the provision of effective icebreaking and pilot services, and optimize the fishery industry structure to prevent sea ice disasters in the Northern China Sea.

Spillover effects from inland waterway transport development: Spatial assessment of the Rhine-Alpine Corridor

Inland waterways have the potential to enhance port-hinterland connectivity and foster sustainable freight transport. Their development as transport corridors is highly dependent on geography, yet spatial spillovers of port throughput are often overlooked. In Europe, the configuration of inland container terminals can lead to cross-border spillovers. This paper analyzes inland container throughput between 2007 and 2021 in the Rhine-Alpine Corridor to quantify its spatial dependence and economic spillover effects. The assessment involves 43 regions from Belgium, France, Germany, Netherlands, Luxemburg, and Switzerland. A Spatial Durbin Model is developed accounting for economic conditions, fixed effects, and time trends. The results suggest that throughput exhibited spatial concentration patterns, which intensified after water levels dropped in 2018. Second, the spatial dependence is heterogeneous between transport performance in tonne-km vs. TEU-km. Third, technological resources and population density were positively associated with throughput, while the opposite was found for employment and motorways. Notably, the spillovers of technological resources and employment were considerably higher than the local impact. Based on these findings, the paper advises on spatially oriented policies to address low water levels, workforce decline, and the need for new business models.

Inter-terminal transportation for an offshore port integrating an inland container depot

Offshore ports, which are located on islands or away from the hinterland, are usually connected with the hinterland via bridges for truck transportation. With the rapid growth of port throughput, the bridges are likely to become a bottleneck restricting the development of port business. Inland container depots (ICD) have been introduced as satellite terminals to address congestion issues on bridges by allowing a proportion of drayage trucks to pick up and drop off containers at the ICD instead of going to the container terminals of the offshore ports. Containers are transported between the ICD and container terminals by inter-terminal transportation (ITT) trucks owned by port authorities. The ICD is expected to mitigate the bridge congestion by transferring the travel demands of drayage trucks in peak hours to the travel demands of ITT trucks in off-peak hours. We propose a methodology for analyzing the influence of ICDs on port efficiency. An integer linear programming model is developed to optimize the movements of containers, drayage trucks and ITT trucks in an offshore port area with the objective of minimizing the costs of drayage trucks and ITT trucks and the penalty for late delivery of containers. Two model transformation approaches are proposed to reduce the solution space of the model for improving the computational efficiency of the algorithm. The influence of ICDs on port efficiency is evaluated through a real-world case study using the proposed methodology. The results provide insights for port authorities to make strategic decisions.

Monitoring and evaluation of ship operation congestion status at container ports based on AIS data

With the rapid development of the shipping industry, many factors, such as larger ships, harsh weather, workers' strikes, surging port throughputs, and the global pandemic, have made port congestion frequent worldwide. How to effectively monitor and evaluate the ship operation congestion status at ports becomes a key challenge for global ports to seek sustainable development. Based on Automatic Identification System (AIS) data, this article analyzed the semantics of ship trajectory by using data mining methods such as the sliding window algorithm, explored ship berthing and berth-waiting operation events, and established models for calculating the congestion index at ports, as well as the time cost, capacity resource cost, and economic cost caused by ship delays to monitor and evaluate the ship operation congestion status at ports. The results showed that the monitoring and evaluation system for container ports based on AIS ship data can effectively monitor and evaluate the port congestion status and support the decision-making efforts of all parties on the supply chain, such as ports, ship enterprises, cargo owners, and shipping agents.

Environmental Sustainability of Inland Freight Transportation Systems in Bangladesh

Bangladesh is committed to perform on Sustainable Development Goals (SDGs) and prioritizing climate change issues to create a safe living and working environment for all. Transport has an enormous contribution to society in functioning our daily life also a cause of environmental degradation. More than 95% of containerized cargo are moving from/to Chattogram Port by using road mode that is liable for Carbon dioxide (CO2) emission, congestion, and high transport cost. Rail intermodal is facing capacity constraints in Dhaka rail Inland Container Depot (ICD), and river intermodal is under-utilized for the shortage of hinterland logistics facilities, the mindset of users, and poor terminal productivity. This research applied the quantitative method to analyze the port throughput of Chattogram Port and its hinterland connectivity by nodes to get innovation and sustainability to identify environmental factors in international freight movement economically. On the basis of a literature review, and explored research gaps before data analysis, it is clear that environmental factors including SDGs are not considered in the inland freight transportation systems of Bangladesh. The research findings alerted that people in the freight movement system do not present a wide awareness of their activities and due diligence to environmental sustainability in creating a wider room for the next generations. The quantitative analysis of port throughput data indicated to increase in the share of intermodal especially river intermodal which has a wider scope to use available river ports in all parts of the country naturally. The practical impact of the research is to formulate a long-term environmental strategy in freight transport, corporate execution by all transport business-oriented companies/organizations, and enabling technologies in inland freight operations and monitoring. Overall, this paper provides a conceptual framework for the environmental sustainability of inland freight transportation systems in Bangladesh in doing international shipping and trade.

Application of economic model optimization algorithm for international trade based on big data technology

Abstract The advent of the era of big data has posed greater challenges to the economic model of international trade and opportunities for international trade. In this paper, from the application of big data in international trade, we study the SVM-based big data clustering analysis method for international trade and propose to optimize the objective function in the iterative process by using the individual fitness function of the chicken flock optimization algorithm for the problem that SVM is easy to fall into the optimal local solution. Then, the steps of international trade big data analysis by constructing CSO-SVM are used to forecast international port throughput, international trade exports, and imports. The average absolute error rate of the trained CSO-SVM model for the prediction of international port throughput decreased to 6.19%, and the accuracy of the prediction of international trade export volume and export price reached 86.86% and 87.83%, respectively, and the accuracy of the prediction of international trade import demand and import price reached 86.71% and 87.07%, respectively. During the test, CSO-SVM has higher prediction performance than other models. The analysis based on big data can better quantify the challenges faced by international trade, optimize international logistics scheduling, and improve import and export planning.

Forecasting long - term container port throughput with a regression model: Case of Dong Nai Port - 2050

The forecast of cargo through the port is not only important for port management but also important for infrastructure planning. In recent years, sea container freight has increased quickly. Therefore, the container is through port forecasting models. The research builds long-term containers through port forecasting models applied to the Dong Nai port as a reference. The study has established correlations between important economic indicators from 2010 to 2020, such as total local product, total export and import turnover, industrial, agricultural value, and foreign direct investment with container volume to build a forecasting model. The model is validated by hindcasting containers through Dong Nai port in 2021, and a suitable forecasting model is selected to forecast the container volume until 2050.