Graph neural network for traffic forecasting: A survey
Traffic forecasting is important for the success of intelligent transportation systems. Deep
learning models, including convolution neural networks and recurrent neural networks, have …
learning models, including convolution neural networks and recurrent neural networks, have …
TBSM: A traffic burst-sensitive model for short-term prediction under special events
Traffic prediction is an important management tool for traffic guidance and control and an
effective decision-making tool to help travelers plan routes and avoid congested road …
effective decision-making tool to help travelers plan routes and avoid congested road …
Hybrid deep learning models for traffic prediction in large-scale road networks
Traffic prediction is an important component in Intelligent Transportation Systems (ITSs) for
enabling advanced transportation management and services to address worsening traffic …
enabling advanced transportation management and services to address worsening traffic …
Spatio-temporal fusion graph convolutional network for traffic flow forecasting
In most recent research, the traffic forecasting task is typically formulated as a spatio-
temporal graph modeling problem. For spatial correlation, they typically learn the shared …
temporal graph modeling problem. For spatial correlation, they typically learn the shared …
Interpretable local flow attention for multi-step traffic flow prediction
Traffic flow prediction (TFP) has attracted increasing attention with the development of smart
city. In the past few years, neural network-based methods have shown impressive …
city. In the past few years, neural network-based methods have shown impressive …
Integrating the traffic science with representation learning for city-wide network congestion prediction
Recent studies on traffic congestion prediction have paved a promising path towards the
reduction of potential economic and environmental loss. However, at the city-wide scale …
reduction of potential economic and environmental loss. However, at the city-wide scale …
Self-Supervised Spatiotemporal Masking Strategy-Based Models for Traffic Flow Forecasting
G Liu, S He, X Han, Q Luo, R Du, X Fu, L Zhao - Symmetry, 2023 - mdpi.com
Traffic flow forecasting is an important function of intelligent transportation systems. With the
rise of deep learning, building traffic flow prediction models based on deep neural networks …
rise of deep learning, building traffic flow prediction models based on deep neural networks …
Confined attention mechanism enabled Recurrent Neural Network framework to improve traffic flow prediction
NS Chauhan, N Kumar - Engineering Applications of Artificial Intelligence, 2024 - Elsevier
Abstract Traffic Flow Prediction (TFP) is one of the most challenging issues and hard-core
requirement for an Intelligent Transportation System (ITS) around the globe. The outcomes …
requirement for an Intelligent Transportation System (ITS) around the globe. The outcomes …
Traffic demand prediction based on spatial-temporal guided multi graph Sandwich-Transformer
The ability of spatial-temporal traffic demand prediction is crucial for urban computing, traffic
management and future autonomous driving. In this paper, a novel Spatial-Temporal Guided …
management and future autonomous driving. In this paper, a novel Spatial-Temporal Guided …
Multi-view teacher with curriculum data fusion for robust unsupervised domain adaptation
Graph Neural Networks (GNNs) have emerged as an effective tool for graph classification,
yet their reliance on extensive labeled data poses a significant challenge, especially when …
yet their reliance on extensive labeled data poses a significant challenge, especially when …