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 …
A survey of traffic prediction: from spatio-temporal data to intelligent transportation
Intelligent transportation (eg, intelligent traffic light) makes our travel more convenient and
efficient. With the development of mobile Internet and position technologies, it is reasonable …
efficient. With the development of mobile Internet and position technologies, it is reasonable …
Graph neural controlled differential equations for traffic forecasting
Traffic forecasting is one of the most popular spatio-temporal tasks in the field of machine
learning. A prevalent approach in the field is to combine graph convolutional networks and …
learning. A prevalent approach in the field is to combine graph convolutional networks and …
Spatial-temporal synchronous graph convolutional networks: A new framework for spatial-temporal network data forecasting
Spatial-temporal network data forecasting is of great importance in a huge amount of
applications for traffic management and urban planning. However, the underlying complex …
applications for traffic management and urban planning. However, the underlying complex …
Traffic prediction using artificial intelligence: Review of recent advances and emerging opportunities
Traffic prediction plays a crucial role in alleviating traffic congestion which represents a
critical problem globally, resulting in negative consequences such as lost hours of additional …
critical problem globally, resulting in negative consequences such as lost hours of additional …
A survey on graph neural networks for time series: Forecasting, classification, imputation, and anomaly detection
Time series are the primary data type used to record dynamic system measurements and
generated in great volume by both physical sensors and online processes (virtual sensors) …
generated in great volume by both physical sensors and online processes (virtual sensors) …
Adaptive graph convolutional recurrent network for traffic forecasting
Modeling complex spatial and temporal correlations in the correlated time series data is
indispensable for understanding the traffic dynamics and predicting the future status of an …
indispensable for understanding the traffic dynamics and predicting the future status of an …
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 …
Deep variational graph convolutional recurrent network for multivariate time series anomaly detection
W Chen, L Tian, B Chen, L Dai… - … on machine learning, 2022 - proceedings.mlr.press
Anomaly detection within multivariate time series (MTS) is an essential task in both data
mining and service quality management. Many recent works on anomaly detection focus on …
mining and service quality management. Many recent works on anomaly detection focus on …