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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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Multi-Scale Dynamic Hypergraph Convolutional Network for Traffic Flow Forecasting
This paper focuses on the problem of traffic flow forecasting, with the aim of forecasting future traffic conditions based on historical traffic...
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Data-Driven Traffic Assignment: A Novel Approach for Learning Traffic Flow Patterns Using Graph Convolutional Neural Network
We present a novel data-driven approach of learning traffic flow patterns of a transportation network given that many instances of origin to...
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Adaptive data processing framework for efficient short-term traffic flow prediction
Accurate short-term traffic forecasting is a prerequisite for establishing intelligent transportation systems. In this paper, a new adaptive traffic...
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A Deep Ensemble Approach for Long-Term Traffic Flow Prediction
In the last 50 years, with the growth of cities and increase in the number of vehicles and mobility, traffic has become troublesome. As a result,...
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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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Traffic Flow Forecasting Based on Transformer with Diffusion Graph Attention Network
Because of the high nonlinearity and complexity, it is still a challenge to forecast traffic flow accurately. Most of the existing methods, which...
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Predicting Traffic Flow with Deep Learning
In transportation systems, a vast volume of traffic data is generated on a daily basis. The contributing factors for this traffic include expanding...
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Traffic Flow Forecasting of Graph Convolutional Network Based on Spatio-Temporal Attention Mechanism
Accurate traffic flow forecasting is a prerequisite guarantee for the realization of intelligent transportation. Due to the complex time and space...
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A hybrid CEEMD-GMM scheme for enhancing the detection of traffic flow on highways
Many highways are acquiring smart transportation systems to improve traffic efficiency, safety and management. Intelligent transportation systems can...
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Prediction of Traffic Flow Based on Calendar Data on Suburban Roads (Case Study: Chalus Road)
Traffic flow prediction as a vital component in the intelligent transportation management systems can significantly improve the overall speed and...
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Models for forecasting the traffic flow within the city of Ljubljana
Efficient traffic management is essential in modern urban areas. The development of intelligent traffic flow prediction systems can help to reduce...
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A Hybrid Framework Combining LSTM NN and BNN for Short-term Traffic Flow Prediction and Uncertainty Quantification
Short-term traffic flow prediction plays a critical role in Intelligent Transportation System (ITS), and has attracted continuous attention. Previous...
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Model Predictive Sliding Mode Control of Highway Traffic Flow: Cooperative and Integrated Approach
The frequent traffic congestion on the highways has necessitated the formulation of a suitable traffic control strategy that reduces transportation...
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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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Artificial intelligence-based traffic flow prediction: a comprehensive review
The expansion of the Internet of Things has resulted in new creative solutions, such as smart cities, that have made our lives more productive,...
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Grey prediction model based on Euler equations and its application in highway short-term traffic flow
As the urbanization rate in China has continued to increase, the highway congestion problem has become more severe, significantly reducing the...
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Predicting traffic propagation flow in urban road network with multi-graph convolutional network
Traffic volume propagating from upstream road link to downstream road link is the key parameter for designing intersection signal timing scheme....
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A self-attention dynamic graph convolution network model for traffic flow prediction
Precise and reliable traffic predictions play a vital role in contemporary traffic management, particularly within complex traffic networks....
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An adaptive model of optimal traffic flow prediction using adaptive wildfire optimization and spatial pattern super learning
Real-time traffic prediction uses past data to anticipate traffic volume. The volume of traffic in the region may be estimated using interpolation...
