A comprehensive survey on applications of transformers for deep learning tasks
Abstract Transformers are Deep Neural Networks (DNN) that utilize a self-attention
mechanism to capture contextual relationships within sequential data. Unlike traditional …
mechanism to capture contextual relationships within sequential data. Unlike traditional …
[HTML][HTML] A fundamental diagram based hybrid framework for traffic flow estimation and prediction by combining a Markovian model with deep learning
Accurate traffic congestion estimation and prediction are critical building blocks for smart trip
planning and rerouting decisions in transportation systems. Over the decades, there have …
planning and rerouting decisions in transportation systems. Over the decades, there have …
Spatio-Temporal Predictive Modeling Techniques for Different Domains: a Survey
Spatio-temporal prediction tasks play a crucial role in facilitating informed decision-making
through anticipatory insights. By accurately predicting future outcomes, the ability to …
through anticipatory insights. By accurately predicting future outcomes, the ability to …
Cool: a conjoint perspective on spatio-temporal graph neural network for traffic forecasting
This paper investigates traffic forecasting, which attempts to forecast the future state of traffic
based on historical situations. This problem has received ever-increasing attention in …
based on historical situations. This problem has received ever-increasing attention in …
Towards Dynamic Spatial-Temporal Graph Learning: A Decoupled Perspective
With the progress of urban transportation systems, a significant amount of high-quality traffic
data is continuously collected through streaming manners, which has propelled the …
data is continuously collected through streaming manners, which has propelled the …
Spatial-temporal graph convolution network model with traffic fundamental diagram information informed for network traffic flow prediction
Accurate and fine-grained traffic state prediction has always been an important research
field. For long-term traffic flow prediction, the high-dimensional and coupled traffic feature …
field. For long-term traffic flow prediction, the high-dimensional and coupled traffic feature …
TCLN: A Transformer-based Conv-LSTM network for multivariate time series forecasting
S Ma, T Zhang, YB Zhao, Y Kang, P Bai - Applied Intelligence, 2023 - Springer
The study of multivariate time series forecasting (MTSF) problems has high significance in
many areas, such as industrial forecasting and traffic flow forecasting. Traditional forecasting …
many areas, such as industrial forecasting and traffic flow forecasting. Traditional forecasting …
A novel combined probabilistic load forecasting system integrating hybrid quantile regression and knee improved multi-objective optimization strategy
Y Yang, Q Xing, K Wang, C Li, J Wang, X Huang - Applied Energy, 2024 - Elsevier
In contemporary power systems, diverse energy integration and technological
advancements have complicated load dynamics, revealing limitations in traditional …
advancements have complicated load dynamics, revealing limitations in traditional …
LaTAS-F: Locality-aware transformer architecture search with multi-source fusion for driver continuous braking intention inference
K Jiang, W Yang, S Huang - Expert Systems with Applications, 2024 - Elsevier
Precise inference of driver braking intention is highly correlated with traffic safety, energy
consumption, and driving comfort of electrified vehicles (EVs). Until recently, gratifying …
consumption, and driving comfort of electrified vehicles (EVs). Until recently, gratifying …
LCDFormer: Long-term correlations dual-graph transformer for traffic forecasting
J Cai, CH Wang, K Hu - Expert Systems with Applications, 2024 - Elsevier
Traffic forecasting has always been a critical component of intelligent transportation systems.
Due to the complexity of traffic prediction models, most research just only consider short …
Due to the complexity of traffic prediction models, most research just only consider short …