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 …
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 hybrid deep learning model with attention-based conv-LSTM networks for short-term traffic flow prediction
Accurate short-time traffic flow prediction has gained gradually increasing importance for
traffic plan and management with the deployment of intelligent transportation systems (ITSs) …
traffic plan and management with the deployment of intelligent transportation systems (ITSs) …
Machine remaining useful life prediction via an attention-based deep learning approach
For prognostics and health management of mechanical systems, a core task is to predict the
machine remaining useful life (RUL). Currently, deep structures with automatic feature …
machine remaining useful life (RUL). Currently, deep structures with automatic feature …
Applications of artificial intelligence in transport: An overview
The rapid pace of developments in Artificial Intelligence (AI) is providing unprecedented
opportunities to enhance the performance of different industries and businesses, including …
opportunities to enhance the performance of different industries and businesses, including …
Machine learning-based traffic prediction models for intelligent transportation systems
A Boukerche, J Wang - Computer Networks, 2020 - Elsevier
Abstract Intelligent Transportation Systems (ITS) have attracted an increasing amount of
attention in recent years. Thanks to the fast development of vehicular computing hardware …
attention in recent years. Thanks to the fast development of vehicular computing hardware …
Artificial intelligence-based hybrid deep learning models for image classification: The first narrative review
Background Artificial intelligence (AI) has served humanity in many applications since its
inception. Currently, it dominates the imaging field—in particular, image classification. The …
inception. Currently, it dominates the imaging field—in particular, image classification. The …
A survey on modern deep neural network for traffic prediction: Trends, methods and challenges
In this modern era, traffic congestion has become a major source of severe negative
economic and environmental impact for urban areas worldwide. One of the most efficient …
economic and environmental impact for urban areas worldwide. One of the most efficient …
Traffic graph convolutional recurrent neural network: A deep learning framework for network-scale traffic learning and forecasting
Traffic forecasting is a particularly challenging application of spatiotemporal forecasting, due
to the time-varying traffic patterns and the complicated spatial dependencies on road …
to the time-varying traffic patterns and the complicated spatial dependencies on road …
A deep learning algorithm using a fully connected sparse autoencoder neural network for landslide susceptibility prediction
The environmental factors of landslide susceptibility are generally uncorrelated or non-
linearly correlated, resulting in the limited prediction performances of conventional machine …
linearly correlated, resulting in the limited prediction performances of conventional machine …