Long sequence time-series forecasting with deep learning: A survey
The development of deep learning technology has brought great improvements to the field
of time series forecasting. Short sequence time-series forecasting no longer satisfies the …
of time series forecasting. Short sequence time-series forecasting no longer satisfies the …
A comprehensive study of speed prediction in transportation system: From vehicle to traffic
In the intelligent transportation system (ITS), speed prediction plays a significant role in
supporting vehicle routing and traffic guidance. Recently, a considerable amount of research …
supporting vehicle routing and traffic guidance. Recently, a considerable amount of research …
Graph construction for traffic prediction: A data-driven approach
JQ James - IEEE Transactions on Intelligent Transportation …, 2022 - ieeexplore.ieee.org
Graph learning-based algorithms are becoming the prevalent traffic prediction solutions due
to their capability of exploiting non-Euclidean spatial-temporal traffic data correlation …
to their capability of exploiting non-Euclidean spatial-temporal traffic data correlation …
Collision avoidance predictive motion planning based on integrated perception and V2V communication
Autonomous vehicles (AVs), as one of the cores in future intelligent transportation systems
(ITSs), can facilitate reliable and safe traffic operations and services. The ability to …
(ITSs), can facilitate reliable and safe traffic operations and services. The ability to …
Traffic prediction with missing data: A multi-task learning approach
Traffic speed prediction based on real-world traffic data is a classical problem in intelligent
transportation systems (ITS). Most existing traffic speed prediction models are proposed …
transportation systems (ITS). Most existing traffic speed prediction models are proposed …
A novel spatio-temporal generative inference network for predicting the long-term highway traffic speed
Accurately predicting the highway traffic speed can reduce traffic accidents and transit time,
which is of great significance to highway management. Three essential elements should be …
which is of great significance to highway management. Three essential elements should be …
DyGCN-LSTM: A dynamic GCN-LSTM based encoder-decoder framework for multistep traffic prediction
Intelligent transportation systems (ITS) are gaining attraction in large cities for better traffic
management. Traffic forecasting is an important part of ITS, but a difficult one due to the …
management. Traffic forecasting is an important part of ITS, but a difficult one due to the …
Spatiotemporal correlation modelling for machine learning-based traffic state predictions: state-of-the-art and beyond
H Cui, Q Meng, TH Teng, X Yang - Transport reviews, 2023 - Taylor & Francis
Predicting traffic states has gained more attention because of its practical significance.
However, the existing literature lacks a critical review regarding how to address the …
However, the existing literature lacks a critical review regarding how to address the …
Gas-insulated switchgear insulation defect diagnosis via a novel domain adaptive graph convolutional network
Convolutional neural networks (CNNs) have promoted the development of insulation defect
diagnosis for gas-insulated switchgear (GIS) because of their excellent feature extraction …
diagnosis for gas-insulated switchgear (GIS) because of their excellent feature extraction …
Traffic prediction with transfer learning: A mutual information-based approach
In modern traffic management, one of the most essential yet challenging tasks is accurately
and timely predicting traffic. It has been well investigated and examined that deep learning …
and timely predicting traffic. It has been well investigated and examined that deep learning …