Sequence-to-sequence recurrent graph convolutional networks for traffic estimation and prediction using connected probe vehicle data
A Abdelraouf, M Abdel-Aty… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Traffic estimation is imperative for conducting fundamental transportation engineering tasks
such as transportation planning and traffic safety studies. Additionally, traffic prediction is …
such as transportation planning and traffic safety studies. Additionally, traffic prediction is …
Improving performance and efficiency of Graph Neural Networks by injective aggregation
W Dong, J Wu, X Zhang, Z Bai, P Wang… - Knowledge-Based …, 2022 - Elsevier
Aggregation functions are regarded as the multiplication between an aggregation matrix and
node embeddings, based on which a full rank matrix can enhance representation capacity of …
node embeddings, based on which a full rank matrix can enhance representation capacity of …
Citywide traffic speed prediction: A geometric deep learning approach
JQ James - Knowledge-Based Systems, 2021 - Elsevier
Accurate traffic speed prediction is critical to modern internet of things-based intelligent
transportation systems. It serves as the foundation of advanced traffic management systems …
transportation systems. It serves as the foundation of advanced traffic management systems …
GCGAN: Generative adversarial nets with graph CNN for network-scale traffic prediction
Traffic prediction is practically important to facilitate many real applications in urban areas
such as relieving traffic congestion. Traditional traffic prediction models are mostly statistic …
such as relieving traffic congestion. Traditional traffic prediction models are mostly statistic …
Traffic flow matrix-based graph neural network with attention mechanism for traffic flow prediction
Traffic flow forecasting is of great importance in intelligent transportation systems for
congestion mitigation and intelligent traffic management. Most of the existing methods …
congestion mitigation and intelligent traffic management. Most of the existing methods …
Domain adversarial graph neural network with cross-city graph structure learning for traffic prediction
Deep learning models have emerged as a promising way for traffic prediction. However, the
requirement for large amounts of training data remains a significant issue for achieving well …
requirement for large amounts of training data remains a significant issue for achieving well …
Deep learning on traffic prediction: Methods, analysis, and future directions
X Yin, G Wu, J Wei, Y Shen, H Qi… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Traffic prediction plays an essential role in intelligent transportation system. Accurate traffic
prediction can assist route planing, guide vehicle dispatching, and mitigate traffic …
prediction can assist route planing, guide vehicle dispatching, and mitigate traffic …
Dl-traff: Survey and benchmark of deep learning models for urban traffic prediction
Nowadays, with the rapid development of IoT (Internet of Things) and CPS (Cyber-Physical
Systems) technologies, big spatiotemporal data are being generated from mobile phones …
Systems) technologies, big spatiotemporal data are being generated from mobile phones …
Enhancing transportation systems via deep learning: A survey
Abstract Machine learning (ML) plays the core function to intellectualize the transportation
systems. Recent years have witnessed the advent and prevalence of deep learning which …
systems. Recent years have witnessed the advent and prevalence of deep learning which …
Multi-range attentive bicomponent graph convolutional network for traffic forecasting
Traffic forecasting is of great importance to transportation management and public safety,
and very challenging due to the complicated spatial-temporal dependency and essential …
and very challenging due to the complicated spatial-temporal dependency and essential …