Daily Ship Traffic Volume Statistics and Prediction Based on Automatic Identification System Data

Wang, Sainan; Wang, Si; Gao, Suixiang; Yang, Wenguo

doi:10.1109/ihmsc.2017.149

Cited by 6 publications

(3 citation statements)

References 13 publications

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“…The generated dataset is analysed with the proposed neural network algorithm and an efficient prediction is obtained. Similarly, Wang et al (2017) focused on the daily ship traffic volume with AIS data. The proposed methods which are Auto-Regressive, Moving Average (ARMA) and Artificial Neural Network (ANN), and hybrid model used for the mining of three different ship types in Shanghai port.…”

Section: Literaturementioning

confidence: 99%

Analysing the Effects of Weather Conditions on Container Terminal Operations Using Machine Learning

Atak

Kaya

Arslanoğlu

2022

International Journal of Business Analytics

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Container ships transport a large number of valuable cargoes, and there is a demand for less expensive and faster transportation options. Weather, vessel type, and the nature and amount of the goods are all external elements that might impact container handling times, which are directly related to overall port stay time. In this scope, container terminal operations could be optimised with the help of historical data which provides access to classification and prediction of the cargo handling operations. In this study, the real-time data of a container terminal operation is analysed with different machine learning techniques along with the Fuzzy C-Means clustering method. The results show that Fuzzy C-Means clustering has a positive impact on the explanatory power of models in container terminal operations. The research revealed that an increase in wind speed influences cargo handling time for mobile cranes.

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Section: Literaturementioning

confidence: 99%

Analysing the Effects of Weather Conditions on Container Terminal Operations Using Machine Learning

Atak

Kaya

Arslanoğlu

2022

International Journal of Business Analytics

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“…Based on the AIS data of crude oil in 2014, Mou et al [22] made an in-depth study on the lag effect of the collapse of oil price on the shipping situation of oil tankers along the Maritime Silk Road, which provided a scientific basis for improving the decision-making ability of crude oil transportation market and formulating maritime operation management measures. Wang et al [23] proposed autonomous statistic methods for counting daily ship traffic volume at ports only based on AIS data, then taking Shanghai port as an instance, they counted the daily ship traffic volume by using the proposed statistic methods for three common types of ship: cargo ship, passenger ship, and tanker ship, which could instruct shipping company to make sound judgment and decision for operational management.…”

Section: ) Relevant Research On Container Shipping Network and Oil Shipping Networkmentioning

confidence: 99%

Assortative Analysis of Bulk Trade Complex Network on Maritime Silk Road

et al. 2020

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Assortativity, one of the mixing patterns of complex networks, is characterized by measuring whether the nodes are preferentially connected to the nodes with a similar scale. While numerous studies have examined the assortative characteristics of various real-world networks, few studies have attempted to analyze the assortativity of networks in which the subject of trade is bulk. The novelty of this research is that, for the first time, the assortative coefficient method in physics is introduced into the bulk trade network, and the Automatic Identification System (AIS) data is used to explore the assortative mixing characteristics of the network. From the perspective of multi-scale (port and country) and multidimensional (node, link, and network) structure, this paper reveals the tendency of trade connection and explores the trade rules of bulk in networks. The results show that: (1) The trade network of bulk on the Maritime Silk Road is assortative. With the increase of spatial scale, the extent of assortativity is also gradually increasing; (2) In the bulk network of ports, trade cooperation shows the rule of distance attenuation; In the national bulk network, it shows the rule of preferential connection; (3) Ports with high out-degree will export bulk to the ports with high out-degree with broad market, while countries with high out-degree export to high in-degree countries with strong demand. The present study is expected to provide valuable references for port planning, national formulation of scientific bulk trade strategy, and promotion of coordinated development of bulk trade network along the Maritime Silk Road.

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“…With the increasing availability and accessibility of sensor data (e.g., tracking data from automatic identification systems (AIS)), scientists and practitioners can make predictions on vessel traffic at fine spatial and temporal levels. Although researchers have formulated numerous prediction methods, these methods tend to focus on predicting traffic volume for specific waterways or ports [3,4], or predicting flow within a short time (e.g., minutes) period. The effectiveness of these methods in providing a relatively longer term (e.g., a lead time of several days) prediction for large areas are unknown.…”

Section: Introductionmentioning

confidence: 99%

A Machine-Learning Model for Zonal Ship Flow Prediction Using AIS Data: A Case Study in the South Atlantic States Region

Wang

2019

JMSE

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Predicting traffic flow is critical in efficient maritime transportation management, coordination, and planning. Scientists have proposed many prediction methods, most of which are designed for specific locations or for short-term prediction. For the purpose of management, methods that enable long-term prediction for large areas are highly desirable. Therefore, we propose developing a spatiotemporal approach that can describe and predict traffic flows within a region. We designed the model based on a multiple hexagon-based convolutional neural network (mh-CNN) model that takes both the flow dynamics and environmental conditions into account. This model is highly flexible in that it predicts zonal traffic flow within variable time windows. We applied the method to measure and predict the daily and hourly traffic flows in the South Atlantic States region by taking the impacts of extreme weather events into consideration. Results show that our method outperformed other methods in daily prediction during normal days and hourly prediction during hurricane events. Based on the results, we also provide some recommendations regarding the future usage and customization of the model.

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Daily Ship Traffic Volume Statistics and Prediction Based on Automatic Identification System Data

Cited by 6 publications

References 13 publications

Analysing the Effects of Weather Conditions on Container Terminal Operations Using Machine Learning

Analysing the Effects of Weather Conditions on Container Terminal Operations Using Machine Learning

Assortative Analysis of Bulk Trade Complex Network on Maritime Silk Road

A Machine-Learning Model for Zonal Ship Flow Prediction Using AIS Data: A Case Study in the South Atlantic States Region

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