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Trajectory Data-Driven Network Representation for Traffic State Prediction using Deep Learning
In this study, we propose a trajectory data-driven network representation method, specifically leveraging directional statistics. This approach...
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Optimized Deep Neural Network Based Intelligent Decision Support System for Traffic State Prediction
Importance of efficient short term traffic state prediction has been increased for accurate traffic planning in the domain of an Intelligent...
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MSSTN: a multi-scale spatio-temporal network for traffic flow prediction
Spatio-temporal feature extraction and fusion are crucial to traffic prediction accuracy. However, the complicated spatio-temporal correlations and...
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Probabilistic Traffic State Prediction Based on Vehicle Trajectory Data
Accurate prediction of traffic flow dynamics is a key step towards effective congestion mitigation strategies. The dynamic nature of traffic flow and...
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A new method of network traffic prediction based on combination model
Network traffic has time-varying and nonlinear characteristics, leading to relatively low accuracy of single linear and nonlinear prediction models....
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Dual flow fusion graph convolutional network for traffic flow prediction
In recent decades, motor vehicle ownership has increased worldwide year by year, which causes that the accurate prediction of traffic flow on urban...
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Traffic State Prediction of Perturbed and Non-perturbed Traffic Scenarios
Traffic state prediction is a regression problem of predicting traffic states such as travel speed, volume, occupancy and density. However, we...
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Intelligent routing method based on Dueling DQN reinforcement learning and network traffic state prediction in SDN
The traditional routing method makes use of limited information on the network links to make routing decisions, which makes it difficult to adapt to...
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Integrating knowledge representation into traffic prediction: a spatial–temporal graph neural network with adaptive fusion features
Various external factors that interfere with traffic flow, such as weather conditions, traffic accidents, incidents, and Points of Interest (POIs),...
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Bi-directional Long Short Term Memory Neural Network for Short-Term Traffic Speed Prediction Using Gravitational Search Algorithm
Traffic speed prediction has implications for urban planning, congestion reduction, and intelligent control systems. To maintain a uniform traffic...
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Optimal RSU deployment using complex network analysis for traffic prediction in VANET
Road Side Units (RSUs) are an integral component of Vehicular ad hoc Networks (VANET) along with connected and autonomous vehicles. RSUs have been...
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A stacked broad learning system with multitask learning method for cellular wireless network traffic prediction
With the development of 5G networks, cellular wireless networks are becoming more diverse and intelligent. As an important part of intelligent...
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A Data-Driven Network Model for Traffic Volume Prediction at Signalized Intersections
Network-wide traffic prediction at the level of an intersection can benefit transportation systems management and operations. However, traditional...
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Recurrence Dynamic Modeling of Metropolitan Cellular Network Traffic
Cellular network traffic analysis is evolving as a pivotal means for detecting anomalous behavior and assisting accurate prediction, which are...
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Federated Learning Based Spatio-Temporal Framework for Real-Time Traffic Prediction
Wireless sensor network is widely explored for traffic flow prediction. Traffic forecasting is a spatio-temporal problem because of the dynamic...
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A Light Weight Traffic Volume Prediction Approach Based on Finite Traffic Volume Data
As one of the key technologies of intelligent transportation systems, short-term traffic volume prediction plays an increasingly important role in...
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Short-Term Traffic Speed Prediction for Multiple Road Segments
Short-term traffic prediction has been an essential part of real-time applications in modern transportation systems for the last few decades. Despite...
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A Multi-Scale Residual Graph Convolution Network with hierarchical attention for predicting traffic flow in urban mobility
Accurate prediction of traffic flow is essential for optimizing transportation resource allocation and enhancing urban mobility efficiency. However,...
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Distributed Core Network Traffic Prediction Architecture Based on Vertical Federated Learning
Network traffic prediction has always been an important research topic, frequently employed in intelligent network operations for load awareness,...
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Implementation of Network Physical Traffic Estimation Model Using 6G Intelligent Traffic Monitoring and Estimation
Data creation and heterogeneity have increased dramatically due to the recent advancements in smart devices. To handle the evolution of network...
