Can maritime big data be applied to shipping industry analysis? Focussing on commodities and vessel sizes of dry bulk carriers

Kanamoto, Kei; Murong, Liwen; Nakashima, Minato; Shibasaki, Ryuichi

doi:10.1057/s41278-020-00171-6

Cited by 17 publications

(7 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…RL framework for freight demand forecasting is proposed by Hassan et al for supporting operational planning (Al Hajj Hassan et al, 2020). Kanamoto et al forecasted future shipping demand based on AIS data from dry bulk vessels using a logit model and RA (Kanamoto et al, 2021). Experimental results have shown the Base Optimization Algorithm (BOA) algorithm outperforms the ant lion optimizer (ALO) for container throughput forecasting in Du et al's (2019) study.…”

Section: Demand and Throughput Forecastingmentioning

confidence: 99%

The Impacts of the Applications of Artificial Intelligence in Maritime Logistics

Aylak¹

2022

European Journal of Science and Technology

View full text Add to dashboard Cite

This study aims to identify current approaches in the usage of Artificial Intelligence (AI) methods for solving shipping problems. Recent advances in AI are being examined, and the way it is adapted to maritime logistics is reviewed. In this study, 66 papers dealing with AI in the maritime industry are reviewed bibliometrically. Research data were primarily sourced from databases of IEEE Xplore, Web of Science, ScienceDirect (Elsevier), Sciences Citation Index, Google Scholar, Springer, and journals. Selected papers are categorized and classified, and the outcomes of some noteworthy publications are discussed in detail. A comprehensive assessment is also presented, which highlights research gaps and forecasts future research orientations. Two possible areas in the maritime industry are proposed for further research using AI capabilities. Predictive analysis is the first domain, followed by energy efficiency optimization. In addition, Machine Learning (ML) and Operations Research (OR) have fostered a growing interest in automating the learning of heuristics to solve optimization problems to avoid the need for expensive and inefficient human labour to create highly specialized heuristics. Future research can take advantage of these new ML approaches to address Maritime Logistics problems utilizing the ever-increasing amount of data available. Future research on maritime logistics can also develop learning models based on the identified gaps.

show abstract

Section: Demand and Throughput Forecastingmentioning

confidence: 99%

The Impacts of the Applications of Artificial Intelligence in Maritime Logistics

Aylak¹

2022

European Journal of Science and Technology

View full text Add to dashboard Cite

show abstract

“…The benefits and necessity of information sharing have been well-recognized by the maritime industry (Zheng et al 2020). The benefits include improvements in cost and time efficiency, reliability, flexibility, responsiveness, resilience and sustainability (Kanamoto et al 2021;Lind 2019;Fruth and Teuteberg 2017). The literature suggests that improved collaboration between maritime logistic actors through better information sharing will reduce the uncertainties along the logistic chain, both in hinterland and foreland, enhance reliability, efficiency, flexibility (Heaver 2015), improve resilience (Shaw et al 2017) and boost performance (Bichou and Gray 2004).…”

Section: Literature Reviewmentioning

confidence: 99%

Information sharing to mitigate delays in port: the case of the Port of Rotterdam

et al. 2021

View full text Add to dashboard Cite

Reliability of service times has long been a concern of many ports around the world. This paper presents an approach to mitigate delays in service times through improved information sharing in ports. The approach is based on a mapping of information sharing links and their association to the root causes of frequently occurring delays. We identify the kind of information which is critical in mitigating delays. Critical information links are then re-ordered to create an information sharing arrangement between the actors, which further condenses and simplifies the required information sharing actions. We apply the proposed approach to the Port of Rotterdam. Quantitative data of 28,000 port calls is complemented by qualitative data collected through direct observations and expert interviews with port actors, including the pilot organization, a tugboat company, the boatmen organization, the harbour master, a terminal and a vessel agent. Besides the suggested arrangement for information sharing, the case reveals the critical position of pilots, a vulnerable position of tugboat companies and the minimal contribution made by the terminal towards information sharing. The increased pressure on ports by ever larger vessels seems to bear its fair share for delays and bottlenecks in the smooth execution of port operations.

show abstract

“…Moreover, the number of businesses that have accumulated and used such data has increased. Several studies have used this data in logistics and maritime economics, as summarized by [2][3][4][5]. Moreover, satellite images have been recently expected to be useful in the maritime industry.…”

Section: Introductionmentioning

confidence: 99%

Terminal Congestion Analysis of Container Ports Using Satellite Images and AIS

Yasuda,

Shibasaki,

Yasuda

et al. 2024

Remote Sensing

Self Cite

View full text Add to dashboard Cite

This study proposes the use of satellite images and a vessel’s automatic identification system (AIS) data to evaluate the congestion level at container ports for operational efficiency analysis, which was never attempted in previous studies. The congestion level in container yards is classified by developing a convolutional neural network (CNN) model and an annotation tool to reduce the workload of creating training data. The annotation tool calculates the number of vertically stacked containers and the reliability of each container cell in a detection area by focusing on the shadows generated by the containers. Subsequently, a high-accuracy CNN model is developed for end-to-end processing to predict congestion levels. Finally, as an example of dynamic efficiency analysis of container terminals using satellite images, the relationship of the estimated average number of vertically stacked containers in the yard with the elapsed time between the image capture time and vessel arrival or departure time obtained from the automatic identification system data is analyzed. This study contributes to representing a prototype for dynamically estimating the number of vertically stacked containers and congestion level of container terminals using satellite images without statistical information, as well as its relationship with the timing of vessel arrival acquired from AIS data.

show abstract

Can maritime big data be applied to shipping industry analysis? Focussing on commodities and vessel sizes of dry bulk carriers

Cited by 17 publications

References 18 publications

The Impacts of the Applications of Artificial Intelligence in Maritime Logistics

The Impacts of the Applications of Artificial Intelligence in Maritime Logistics

Information sharing to mitigate delays in port: the case of the Port of Rotterdam

Terminal Congestion Analysis of Container Ports Using Satellite Images and AIS

Contact Info

Product

Resources

About