A New Perspective on Traffic Flow Prediction: A Graph Spatial-Temporal Network with Complex Network Information

Hu, Zhiqiu; Shao, Fengjing; Sun, Rencheng

doi:10.3390/electronics11152432

Cited by 9 publications

(4 citation statements)

References 32 publications

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“…A characteristic feature of today is the emergence of network, and in the future, "cloud" production. Such technologies require the development of modern approaches to organization and planning, which are subject to a shift in emphasis from automation of operations to automation of management, including all stages of the life cycle [12][13][14][15].…”

Section: Multicriteria Assessment Methodsmentioning

confidence: 99%

Multicriteria Assessment Method for Network Structure Congestion Based on Traffic Data Using Advanced Computer Vision

Ekhlakov,

Andriyanov

2024

Mathematics

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Overloading of network structures is a problem that we encounter every day in many areas of life. The most associative structure is the transport graph. In many megacities around the world, the so-called intelligent transport system (ITS) is successfully operating, allowing real-time monitoring and making changes to traffic management while choosing the most effective solutions. Thanks to the emergence of more powerful computing resources, it has become possible to build more complex and realistic mathematical models of traffic flows, which take into account the interactions of drivers with road signs, markings, and traffic lights, as well as with each other. Simulations using high-performance systems can cover road networks at the scale of an entire city or even a country. It is important to note that the tool being developed is applicable to most network structures described by such mathematical apparatuses as graph theory and the applied theory of network planning and management that are widely used for representing the processes of organizing production and enterprise management. The result of this work is a developed model that implements methods for modeling the behavior of traffic flows based on physical modeling and machine learning algorithms. Moreover, a computer vision system is proposed for analyzing traffic on the roads, which, based on vision transformer technologies, provides high accuracy in detecting cars, and using optical flow, allows for significantly faster processing. The accuracy is above 90% with a processing speed of more than ten frames per second on a single video card.

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Section: Multicriteria Assessment Methodsmentioning

confidence: 99%

Multicriteria Assessment Method for Network Structure Congestion Based on Traffic Data Using Advanced Computer Vision

Ekhlakov,

Andriyanov

2024

Mathematics

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“…Han and Gong [17] embed the LSTM model into the GCN parameters. Hu, Shao and Sun [18] proposed a graph space-time network (GSTNCNI) incorporated complex network feature information, is proposed to predict future highway traffic flow time series. Their models greatly reduce the amount of computation and make a good use of the temporal and spatial information of the California highway data.…”

Section: Literature Reviewmentioning

confidence: 99%

Deep Learning Architecture for Flight Flow Spatiotemporal Prediction in Airport Network

2022

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Traffic flow prediction is a significant component for the new generation intelligent transportation. In the field of air transportation, accurate prediction of airport flight flow can help airlines schedule flights and provide a decision-making basis for airport resource allocation. With the help of Deep Learning technology, this paper focuses on the characteristics of flight flow easily disturbed by environmental factors, studies the spatiotemporal dependence between flight flows, and predicts the spatiotemporal distribution of flight flows from the airport network level. We proposed a deep learning architecture named ATFSTNP, which combining the residual neural network (ResNet), graph convolutional network (GCN), and long short-term memory (LSTM). Based big data analytics of air traffic management, this method takes the spatiotemporal causal relationship between weather impact and flight flow as the core, and deeply mines the complex spatiotemporal relationship of flight flow. The model’s methodologies are improved from the practical application level, and extensive experiments conducted on the China’s flight operation dataset. The results illustrate that the improved model has significant advantages in predicting the flight flow under weather affect. Even in the complex and variable external environment, the model can still accurately predict the spatiotemporal distribution of the airport network flight flow, with strong robustness.

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“…Network traffic analysis can also provide valuable insights for businesses. Organizations can identify usage trends and patterns by understanding the data flow across a network, which can inform decision-making processes [9]. This knowledge can help optimize network usage, reduce costs, and improve productivity.…”

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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A New Perspective on Traffic Flow Prediction: A Graph Spatial-Temporal Network with Complex Network Information

Cited by 9 publications

References 32 publications

Multicriteria Assessment Method for Network Structure Congestion Based on Traffic Data Using Advanced Computer Vision

Multicriteria Assessment Method for Network Structure Congestion Based on Traffic Data Using Advanced Computer Vision

Deep Learning Architecture for Flight Flow Spatiotemporal Prediction in Airport Network

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

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