Maritime Traffic as a Complex Network: a Systematic Review

Álvarez, Nicanor García; Adenso‐Díaz, Belarmino; Calzada-Infante, Laura

doi:10.1007/s11067-021-09528-7

Cited by 35 publications

(6 citation statements)

References 148 publications

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“…The analysis methods of complex network theory in risk assessment are summarized as follows. One method is to build a real network and analyze the topology of the network [19] . For example, maritime accident analysis [20][21] , maritime traffic situation analysis [22][23] [24] , railroad accident analysis [25] , and construction accident analysis [26] .…”

Section: Related Workmentioning

confidence: 99%

A propagation model of ship collision causation based on complex network dynamics theory

Liu,

Zhu,

Yang

et al. 2024

Seventh International Conference on Traffic Engineering and Transportation System (ICTETS 2023)

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In order to reveal the ship collision causation propagation law, a ship collision causation propagation model is proposed in this paper based on the complex network dynamics theory. The model improved the Susceptible—Infected—Recovered (SIR) model and makes it applicable to the dynamic process of risk propagation. In order to apply the model to the actual network, we construct a ship collision causation network. The network is used as the simulation object of this study. The simulation results show that the causative nodes of different importance have the ability to propagate the risk to the whole network. The impact of accident causation can be reduced by shortening the inspection time of accident causation, reducing the risk propagation rate, increasing the risk control rate, and selecting appropriate initial control factors. The model can help to simulate the propagation of the causes of ship collisions, and can quantitatively analyze the impact of the causes on ship collisions.

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Section: Related Workmentioning

confidence: 99%

A propagation model of ship collision causation based on complex network dynamics theory

Liu,

Zhu,

Yang

et al. 2024

Seventh International Conference on Traffic Engineering and Transportation System (ICTETS 2023)

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“…scitation.org/journal/cha Wu et al, 2019;Cheung et al, 2020;Ducruet, 2020a;2020b;and Álvarez et al, 2021).…”

Section: Articlementioning

confidence: 99%

Link prediction of the world container shipping network: A network structure perspective

Wang

Shi

2021

Chaos: An Interdisciplinary Journal of Nonlinear Science

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Although the world container shipping network (WCSN) has gradually been shaped with ever-increasing complexity in link evolution over the last decades, its evolving mechanism remains to be unveiled. This motivates us to explore the evolutionary pattern of the WCSN, which can be achieved by advancing the existing link prediction models. Using the k-shell decomposition method, the network hierarchy can be decomposed and evaluated by four indices which are KS-Salton, KS-AA, KS-RA, and KS-LRW. The results show that the network hierarchy depends largely on trade patterns and demonstrates certain geographic characteristics. Meanwhile, the KS-LRW index performs best and, therefore, is further simulated for the future WCSN by predicting its top 1677 potential edges, which significantly enhances the overall network connectivity and efficiency. These findings create profound implications for shipping companies to strategically reduce the trail cost for new lines by analyzing the network data.

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“…Network traffic is continuously increasing, and as our dependence on the internet grows, it has become essential to understand its complexities and demands better. Complex network traffic analysis is studying and understanding the patterns and characteristics of data flow across a network [3]. It involves collecting, analyzing, and interpreting large amounts of data to gain insight into how a network is utilized.…”

Section: Introductionmentioning

confidence: 99%

Multilayer Seasonal Autoregressive Integrated Moving Average Models for Complex Network Traffic Analysis

Ravanappan,

Tiwari

et al. 2024

JMC

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The ever-increasing amount of network traffic generated by various devices and applications has made it crucial to have efficient methods for analyzing and managing network traffic. Traditional approaches, such as statistical modeling, have yet to be proven enough due to network traffic's complex nature and dynamic characteristics. Recent research has shown the effectiveness of complex network analysis techniques for understanding network traffic patterns. This paper proposes multilayer seasonal autoregressive integrated moving average models for analyzing and predicting network traffic. This approach considers the seasonal patterns and interdependencies between different layers of network traffic, allowing for a more accurate and comprehensive representation of the data. The Multilayer Seasonal Autoregressive Integrated Moving Average (MSARIMA) model consists of multiple layers, each representing a different aspect of network traffic, such as time of day, day of week, or type of traffic. Each layer is modeled separately using SARIMA, a popular time series forecasting technique. The models for different layers are combined to capture the overall behavior of network traffic. The proposed approach has several benefits over traditional statistical approaches. It can capture network traffic's complex and dynamic nature, including short-term and long-term seasonal patterns. It also allows for the detection of anomalies and the prediction of future traffic patterns with high accuracy.

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Maritime Traffic as a Complex Network: a Systematic Review

Cited by 35 publications

References 148 publications

A propagation model of ship collision causation based on complex network dynamics theory

A propagation model of ship collision causation based on complex network dynamics theory

Link prediction of the world container shipping network: A network structure perspective

Multilayer Seasonal Autoregressive Integrated Moving Average Models for Complex Network Traffic Analysis

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