Largest Container Port Research Articles

Optimizing container ports’ transportation and Distribution systems toward a low-carbon future: A Shenzhen port case study

Although the large throughput of China’s container ports signals positive progress on trade and economic growth, large Chinese container ports still heavily rely on high-emitting and heavy-polluting diesel trucks for transportation and distribution. The reliance on diesel HDTs is aggravating air pollution in ports’ surrounding areas, worsening traffic congestion, and preventing port cities from establishing ambitious carbon-emission reduction targets.

THE GREEN PORTS FUTURE KNOWLEDGE MAP AND TREND

The purpose of this study is to track the emergence of research related to green ports over time and determine the knowledge map and trends of green port research topics in the future using thematic map bibliometric analysis. This study also provides an overview of the researchers and contributors of research countries in green port research globally. This research provides a comprehensive bibliometric analysis of historical developments and current trends in green port research, by examining 462 articles published from 1991-2023 on the Scopus database. This research uses R studio Cloud bibliometric analysis and biblioshiny package. It was found that there are three phases in green port research, namely the initial stagnation phase (1991-2006), infant growth (2007-2015), and expanding phase (2016-2022), the number of articles increased quite rapidly in the third phase until now. This research also highlights related to authors, contributor countries and it was found that the country of China contributed the most contributor authors compared to other countries, this is because China is a country that places many container ports as the top 10 largest container ports in the world, with a total market share reaching 70% of the global. From this analysis, it is found that the knowledge map and the direction of future topics related to green ports are “sustainable development”, “port development”, “port operation”, “carbon emissions” and “renewable energy resources”, besides that it is also found that there is a direction of the current green port and “smart port” research trends. So, it can be recommended that in the future these topics will be very relevant to develop, the themes of research themes needed include alternative strategies in net zero emission (NZE) in ports in the operational and development aspects of the port.

A Study of the Efficiency of Mediterranean Container Ports: A Data Envelopment Analysis Approach

The current paper presents the results of a study that analyzed and evaluated the efficiency of the largest container ports in the wider area of the Mediterranean Basin. The research question that this paper seeks to respond to is how the resources (inputs) of a container port reflect its level of activity and efficiency. In particular, what is the relationship between ports’ infrastructures, equipment and their productivity and the ports’ ability to attract economic activities as well as the extent of their effect on a port’s efficiency? The methodology uses the data envelopment analysis (DEA) output-oriented model following a cross-sectional approach. The research conducts two modeling approaches, the CCR and the BCC model. The analysis goes deeper and compares port efficiency estimates in relation to medium-sized and large ports’ classification and their total market share. The main findings indicated an average efficiency of 0.88 and 0.89 assuming constant and variable returns of scale, respectively, implying that the ports can increase their output levels up to approximately 1.2 times without any change in their inputs.

Forecasting future electric power consumption in Busan New Port using a deep learning model

As smart and environmentally friendly technologies and equipment are introduced in the sea port industry, electric power consumption is expected to rapidly increase. However, there is a paucity of research on the creation of electric power management plans, specifically in relation to electric power consumption forecasting, in ports. In order to address this gap, this study forecasts future electric power consumption in Busan New Port (South Korea's largest container port) and, comparing this with the current standard electric power supply capacity, investigated the feasibility of maintaining a stable electric power supply in the future. We applied a Long Short-Term Memory (LSTM) model trained using electric power consumption and throughput data of the last 10 years to forecast the future electric power consumption of Busan New Port. According to the results, electric power consumption is expected to increase at an annual average of 4.9 % until 2040, exceeding the predicted annual 4.7 % increase in throughput during the same period. Given these results, the current standard electric power supply capacity is forecast to reach only 35 % of demand in 2040, indicating that additional electrical power supply facilities will be needed for stable port operation in the future.

Quantifying the impact of pandemic lockdown policies on global port calls.

The recent experience of lockdowns during COVID-19 highlights the prolonged impact a pandemic could have on ports and the shipping industry. This paper uses port call data derived from the Automatic Identification System (AIS) reports from the world’s 30 largest container ports to quantify both the immediate and longer-term impact of national COVID-19 lockdown policies on global shipping flows. The analysis uses the Difference-in-Difference (DID) and combined regression discontinuity design (RDD)-DID models to represent the effects of lockdown policies. The combination of RDD and DID models is particularly effective because it can mitigate time trends in the data, e.g., the Chinese New Year effect on Chinese ports. This study further examines the potential shock propagation effects, namely, how lockdown policy in one country (i.e., China) can affect the number of port calls in other countries. We categorize ports in other countries into a high-connectivity (with Chinese ports) group and a low-connectivity group, using a proposed connectivity index with China derived from individual vessel trajectories obtained from the AIS data. The results provide a clearly measurable picture of the kinds of trade shocks and consequent pattern changes in port calls over time caused by responses to lockdown policies of varying levels of stringency. We further document the existence of significant shock propagation effects. As the risk of pandemics rises in the twenty-first century, these results can be used by policy makers to assess the potential impact of different levels of lockdown policy on the maritime industry and trade flows more broadly. Maritime players can also use findings such as these to manage their capacity during lockdowns more effectively and to respond more flexibly to changing demand in seaborne transportation.

Efficiency of DECA on ship emission and urban air quality: A case study of China port

As the largest container port in northern China, the prosperous maritime trade has not only promoted the economic development of Qingdao port but also brought serious pollution to the air quality. Ship emissions have caused a significant influence on public health. The establishment of the Domestic Emission Control Area (DECA) is a key policy to reduce ship emissions in recent years. It has been implemented in Qingdao Port from January 1, 2019. To evaluate the environmental benefits of the DECA policy, this study established a bottom-up ship emission inventory in 2020 for Qingdao based on Automatic Identification System (AIS) data. Changes in ship emissions and their impact on air quality in Qingdao are evaluated for the same study area before and after the implementation of DECA policy. The total emissions in all study area for SO2, NOX, PM10, PM2.5, HC and CO in 2020 are 8.16 × 103, 3.31 × 104, 1.25 × 103, 1.15 × 103, 1.18 × 103, 2.15 × 103 tons (t), respectively, reductions of 67.9%, 15.4%, 63.2%, 62.9%, 27.2% and 23.2% compared with 2016, and reductions of 73.42%, 29.97%, 69.54%, 69.26%, 39.71%, 36.44% compared with the scenario without DECA in 2020. The inter-month change and spatial distribution of ship emissions are similar in both years. Shinan District, Shibei District and Huangdao District are most affected by ship emissions, with the largest influence in summer. The ship's contribution to air pollution in each area had been significantly reduced, especially for SO2 and PM after DECA policy. However, the emission reduction of other pollutants is limited, so it is necessary to further enhance the DECA policy. Our research aims to conduct a comprehensive assessment of the environmental benefits for DECA policy, and provide support for the further improvement of the subsequent measures.

Ship emissions in hotelling phase and loading/unloading in Southeast Asia ports

The rapid increase in ship calls and container throughput in Southeast Asia's container port system severely affects the regional environment. This study investigates ship emissions in the hotelling phase, relating mainly to loading and unloading activities at berths. Ship emissions at berths were calculated using the bottom-up approach based on the number of ship calls, power of auxiliary engines, load factor, emissions factor, and in-port hotelling duration of the top ten ports in the region from 2016 to 2020. With a high density of ships and continuity in loading/unloading operations, the Southeast Asia container port system, with several large container ports such as Singapore, Port Kelang, and Tanjung Pelepas, produces enormous emissions, causing air pollution. Further, increased volume of containers and ship calls leads to high pollution in the Southeast Asia container port system, whereas enhanced loading/unloading productivity of ports reduces emissions per TEU.

Lessons learned from an IoT deployment for condition monitoring at the Port of Felixstowe

The ports sector is critical to global trade. While digitalisation of infrastructure asset management in other sectors such as manufacturing, healthcare, water supply, railway and road is rapidly growing with the possibilities of the Internet of Things (IoT) solutions, the maritime industry lags significantly behind. IoT solutions and the near real-time data they produce provide new impetus to improve fault diagnosis of assets and prevent disruptions caused due to asset breakdown. Such solutions also require reliable communication systems to support low latency and high bandwidth. To this end, we are building an IoT-based asset management solution at the Port of Felixstowe, the UK's largest container port, using 5G technology. This paper presents the steps taken, challenges faced and the lessons learned with sourcing, installation, calibration and communication of sensors in this deployment.

Handling Irregularly Sampled IoT Time Series to Inform Infrastructure Asset Management

Infrastructure systems in today's increasingly interconnected world employ the capabilities of the Internet of Things (IoT) technologies for their monitoring, operational control, and asset management. IoT devices can be defined as sensors (of different types) collecting, processing, and sharing time series of data. The analysis of such data often face challenges as a consequence of the high frequency of data collection and the increasing number of sensors placed on infrastructure. Power related issues, timestamp misalignment, and heterogeneous sampling designs are among the most common issues that the IoT data collection may suffer alongside the inherent complexities of large scale databases. This paper provides an overview of time series mining techniques adapted to tackle such issues in IoT data. The aim is to have a pattern recognition tool-set for developing anomaly detection algorithms. Particularly, the paper investigates how to efficiently handle large-scale time series coming from multiple sensors in a stream and following an unevenly spaced - irregular - sampling. The analysis is demonstrated through a case study of time series data mining of sensors installed for supporting the predictive maintenance of quay-cranes at the Port of Felixstowe, the largest container port in Britain.

APPLICATION OF FUZZY TOPSIS MODEL FOR CONTAINER PORT SELECTION CONSIDERING ENVIRONMENTAL FACTORS

Container ships are the type of ships that produce the most emissions in maritime transport. In container transportation not only in navigation but also at container ports, a lot of emissions are formed. Ports are generally close by and highly interacted with the inner parts of the city, the impacts of the gas emissions are quite high on people. The study investigated port selection criteria, in terms of cost, efficiency and especially in terms of environmental impacts in order to create awareness. Container port selection problem was solved using fuzzy TOPSIS (The Technique for Order Preference by Similarity to Ideal Situation) method considering the perspective of carriers. In the study, the container port selection was carried out among the four largest container ports in Turkey.

Spatial and Temporal Patterns of Stony Coral Tissue Loss Disease Outbreaks in The Bahamas

Coral reefs of Grand Bahama and New Providence islands in The Bahamas have been surveyed several times over the past decade, and long-term monitoring indicates declines in coral cover associated with hurricanes, bleaching events, and local threats. However, the greatest declines in coral populations in The Bahamas over the past decade may be attributed to the recent introduction of stony coral tissue loss disease (SCTLD). In 2019, a comprehensive assessment of both islands was conducted using Atlantic and Gulf Rapid Reef Assessment (AGRRA) methods to characterize reefs before SCTLD was reported in The Bahamas. Following reports of SCTLD in late 2019, timed roving diver assessments of corals were conducted for Grand Bahama in March 2020 and New Providence in June 2020 to determine which species were affected by the disease and the proportion of corals that were healthy, infected with SCTLD, and those that appeared to have experienced recent mortality for the most abundant intermediate or highly susceptible species. Additional surveys were conducted for both islands in January 2021 to further assess the extent of the outbreak, and repeated assessments of several sites for each island were used to determine the impact of the disease on corals over the previous 6.5 to 10.5 months. Infection rates varied among species following patterns described for Florida and elsewhere, with higher infection rates occurring in vulnerable species for both Grand Bahama and New Providence. Pseudodiploria strigosa appears to be the most affected species with 45.6% of colonies on Grand Bahama infected and 23.1% infected on New Providence and recent mortality rates of 31.5 and 42.7%, respectively, at the time of surveys. Spatial patterns of mortality and infection rates for the most vulnerable species were greatest close to international commercial shipping ports on both islands, suggesting SCTLD has been present in those locations for a longer time, and the proportion of healthy colonies increased with distance from the port. For Grand Bahama, there was also a significant effect of depth, with shallow reefs having a higher proportion of colonies that was infected or experienced recent mortality. For New Providence, sites to the east of the port saw a sharp decline in infection and mortality rates with distance compared to sites west of the port, where nearly the entire coastline was affected by SCTLD. Temporal analyses showed an increase in recent mortality and a decrease in active infection for most species on both islands, but little change in the proportion of healthy corals, suggesting some degree of resistance to the disease. Because Freeport and Nassau are the two largest container ports in The Bahamas and are over 200 km apart with multiple islands between them where SCTLD has not yet been reported, it is probable that SCTLD arrived in The Bahamas via commercial shipping, followed by rapid spread within islands via local currents and other vectors. Results from this study stress the need for early detection and suggest that preventing the spread of the disease between islands via vessel traffic is of utmost importance.

Key Performance Indicators Analysis for Quay Container Crane Performance Assessment (Case Study at Jakarta International Container Terminal)

<p><span lang="PT-BR">PT Jakarta International Container Terminal (JICT) is the largest container port in Indonesia. Currently, JICT capacity is 2.5 million TEUs (Twenty-foot Equivalent Units) per year, it continues to strive to improve international services and is supported by adequate container loading and unloading equipment. The requirement to establish common standards in different types of container port equipment and identify performance indicators to assess the performance of container handling equipment has increased. Although the Quay Container Crane (QCC) operating system may be different at each container terminal, there are similarities in its main movements, namely: Main Hoist, Trolley, Gantry, and Boom. By knowing the clock metric for each movement, it is possible to determine the Key Performance Indicator (KPI) that has been adopted and assess the performance of the Quay Container Crane (QCC). The results of the study identified that the value of MMBF (Mean Move Between Failures) decreased due to the accumulation of long-lasting heavy load operations, while the number of maintenance activities for machine parts and working hours continued to increase. Key Performance Indicator (KPI) as a management tool can guide QCC inspections and the results can provide useful insights for improving the performance of equipment and container loading and unloading operations in the future.</span></p>

Tugboat scheduling problem in large container ports: A case study of the Singapore port

In recent years, more and more container ports have become congested because of the large number of visiting ships to the ports. This causes a difficult dispatching and management problem for port operators, that is, the tugboat scheduling problem. To solve the problem effectively from the operational viewpoint, this study proposes an interesting tugboat scheduling problem in a large container port. A mixed-integer linear programming (MILP) model is built for the problem with the objective function of minimizing the total processing cost for container ships. We also consider the constraints of horsepower limitation of tugboats, utilization time limit of tugboats, etc. By using the commercial solver CPLEX to solve the built MILP model in a case study of the Singapore Port, the efficiency of the MILP model is presented. It takes less than 18 hours to reduce the optimality gap to a maximum of 0.9% for solving a real-size instance with 12 tugboats and 100 ships. Moreover, the results of extensive computational experiments also show the applicability and effectiveness of the MILP model to handle real-world tugging operations.

Alternative Approach for Improvement Sustainable Supply Chain Management in the Large European Container Ports

The main objectives of the EU transport policy belongs the limitation of the negative environmental impact from ports. Similarly, companies are adopting sustainable supply chain management practices to response the policy makers’ and consumers’ demands for sustainable operations. This paper aims to discover how the largest European container ports communicate about their efforts to improve the sustainability of their operations to find out how the ports themselves see their position as a part of transition towards more sustainable supply chain operations. Based on the study, different large European container ports consider environmental issues variously. The risk is that some ports may get competitive advantages by slipping in the environmental questions. Alternatively, if the port does not take sustainability questions seriously and it gets a bad reputation, the risk is that the customers and consumers do not accept the behavior of the port and shipping companies start to avoid that port. Doi: 10.28991/HIJ-2021-02-02-06 Full Text: PDF

Assessing the Impact of Disruptive Events on Port Performance and Choice: The Case of Gothenburg

This paper assesses the impact of a major disruptive event at the port of Gothenburg, Scandinavia’s largest container port. Automatic Identification System (AIS) data is analyzed, in combination with official port statistics on container handling in the four main container ports in Sweden, from 2014–2018. Particular attention is paid to the relationship between container volumes handled and calculated performance metrics at the specific times of the intense labour dispute at the port of Gothenburg during the periods Q2 (2016) and Q4 (2016)–Q2 (2017). The paper concludes that the decline in container volumes handled at Gothenburg over the period is specifically due to fewer ships calling at the port following each of the intense periods of the labour dispute. It is also concluded that the effect on competitor ports in the region were significant in terms of both increased volumes of gateway container traffic and the resulting short-term and medium term impacts on both port user profiles and port efficiency levels.

Sử dụng dữ liệu nhận dạng tự động (AIS) ước tính lượng khí phát thải của tàu thuyền hoạt động tại thành phố Hồ Chí Minh

The shipping industry, one of the least regulated sources of artificial emissions, has grown rapidly over the past decades. Increasing ship traffic has significantly affected the air quality in areas around seaports and navigational channels, especially in coastal areas. By processing the data set from the Automated Identification System (AIS) provided by Marinetraffic, this study has built a secondary database of high-resolution ship emissions for Ho Chi Minh City, Vietnam, which was ranked 26th in the top 50 largest container ports in the world, according to the 2018 ranking of The Journal of Commerce (JOC),. The amount of CO, CO2, NOx, and SO2 emissions across the region from June to November 2019 is estimated at 0.82; 489.74; 2.69; 1.13x103 tons. Cruising mode of ships are the largest contributor in the total emissions of 43-47%, followed by the berths that also contribute a significant amount of emissions from 34-41%. By ship type, container ships are the largest group with 58104 ships and emit the most 210.87x103 tons of emissions, followed by dry tankers and oil tankers. These three types of ships account for 73.4% of total emissions. The specific Bottom-up approach is the STEAM model that was used to calculate the emission levels of 7 different ship groups. The results of the study can be used to propose effective solutions to limit the impact of ship emissions on Ho Chi Minh City's air quality.

Tugboat scheduling under ship arrival and tugging process time uncertainty

This study addresses an interesting tugboat scheduling problem considering uncertainty in both container ship arrival and tugging process times for large container ports. The uncertain ship arrival and tugging process times are formulated as a finite set of discrete scenarios that can be generated from historical port traffic data. We deal with the uncertainty by integrating proactive and reactive scheduling strategies such that this study is distinct from most existing studies in the literature. The proactive scheduling strategy considers the expected degree of variability and uncertainty during the execution of a tugboat fleet schedule while the reactive scheduling strategy properly adjusts the initial schedule to cope with unexpected scenarios with minimum recovery cost. A mixed-integer linear programming model for the proposed tugboat scheduling problem is established. For a large-scale problem, an ad-hoc algorithm is designed to generate tugging chains such that the large-scale problem can be tackled effectively. The extensive numerical experiments are finally carried out to demonstrate the practical significances of the models and algorithms developed by this study.

Port-Related Emissions, Environmental Impacts and Their Implication on Green Traffic Policy in Shanghai

The port of Shanghai, as the world’s largest container port, has been experiencing rapid development in recent years, with increasing cargo throughput capacity. The combustion of diesel fuels used by internal and external port-related container trucks and in-port machineries can release various pollutants, causing air pollution. The terminals are close to the residential area, and the emissions are concentrated, which is worth paying attention to. This study aims to synthetically assess the port-related emissions and their environmental impacts. We firstly constructed an emission inventory of air pollutants in the port of Shanghai and then used the WRF-CMAQ model to estimate the influence of port-related source emissions on air quality. The results show that the annual emissions of SO2, NOX, CO, VOCS, PM, PM10, PM2.5, CO2, BC and OC caused by cargo-handling equipment were 21.88 t, 1811.22 t, 1741.72 t, 222.76 t, 61.52 t, 61.42 t, 58.41 t, 141,805.40 t, 26.80 t and 10.07 t in 2015. The emissions of NOX, CO, VOCS, PM10 and PM2.5 caused by external port-related container trucks were 18,002.92 t, 5308.0 t, 1134.57 t, 711.12 t and 640.58 t. The exhaust of external port-related container trucks was much larger than that of cargo-handling equipment, so the impact on air quality was also higher than that of the machinery. The peak annual average concentrations of PM2.5 and NOX contributed by the port-related sources were 1.75 μg/m3 and 49.21 μg/m3, respectively, which accounted for 3.08% and 36.7%, respectively, of the simulated ambient concentrations by all the anthropogenic emissions in Shanghai. Our results imply that the emission control policy to reduce the combined port-related emissions, especially for the cargo-delivery transportation phase from port to city, is key for large coastal port cities such as Shanghai.

Aerosol tracking using lidar-based atmospheric profiling and Bayesian estimation

Soot aerosol in atmosphere is one of deadliest forms of air pollution and is a major cause for health problems according to EPA. Compact light detection and ranging (lidar) systems can be used to obtain aerosol profile measurements by identifying aerosol scattering ratios as function of altitude. Aerosol ratio parameters are known to vary with aerosol type, size, and shape. This work employs a ground-based lidar system to detect the source of soot emissions in atmosphere in a wide area around the campus of Old Dominion University (ODU), Norfolk, VA. Different aerosol scattering ratio parameters including lidar and color ratios are obtained from collected lidar data around the campus and these ratios are analyzed for detection and quantification of soot aerosol. A Bayesian estimation algorithm was chosen, after extensive study, to determine the source of the soot in the measurements by tracking the pollution concentration. Results of the analysis using lidar data show that the source of soot pollution is a nearby major Hampton Blvd. Hampton Blvd. is a major arterial with traffic signals through the ODU campus where diesel trucks frequently travel to serve Port of Virginia, which ranks as the third largest container port on the East Coast.

Capacity analysis of ship-tugging operations in a large container port

Abstract In this study, we deal with the simulation problem of ship-tugging operations in a large container port. First, we build a tugboat-service network using a directed graph, where the nodes consist of tugboat bases, anchorages, and berths while the arcs represent the directed links between nodes. Second, we develop a marine traffic simulation system, which can simulate maritime traffic conditions in restricted waters, such as large container ports and busy straits. The marine traffic simulation system is a customized system. Users are allowed to set their study areas and virtual traffic environment (such as traffic demand and network structure) at will. Finally, we conduct various simulations for a real tugboat-service network of Singapore Port, which includes one anchorage, one tugboat base, two berths, and two channels between the anchorage and berths. A detailed analysis of the tugging capacity is provided based on simulation results.