Short-term traffic prediction using deep learning long short-term memory: Taxonomy, applications, challenges, and future trends
This paper surveys the short-term road traffic forecast algorithms based on the long-short
term memory (LSTM) model of deep learning. The algorithms developed in the last three …
term memory (LSTM) model of deep learning. The algorithms developed in the last three …
Traffic management approaches using machine learning and deep learning techniques: A survey
Traffic management is improved in cutting-edge smart cities using technologies such as
machine learning and deep learning to streamline daily tasks and boost productivity …
machine learning and deep learning to streamline daily tasks and boost productivity …
Mt-stnet: A novel multi-task spatiotemporal network for highway traffic flow prediction
Multi-step highway traffic flow prediction is crucial for intelligent transportation systems, and
existing works have made significant advancements in this field. However, the physical …
existing works have made significant advancements in this field. However, the physical …
Adaptive spatiotemporal inceptionnet for traffic flow forecasting
Traffic flow forecasting is crucial to Intelligent Transportation Systems (ITS), particularly for
route planning and traffic management. Spatiotemporal graph neural networks have been …
route planning and traffic management. Spatiotemporal graph neural networks have been …
PKET-GCN: prior knowledge enhanced time-varying graph convolution network for traffic flow prediction
Due to prediction on the traffic flow is influenced by the real environment and historical data,
the produced traffic graph may include significant uncertainty. The graph convolution …
the produced traffic graph may include significant uncertainty. The graph convolution …
Short-term traffic flow prediction based on a hybrid optimization algorithm
A novel least squares twin support vector regression method is proposed based on the
robust L 1-norm distance to alleviate the negative effect of traffic data with outliers. Although …
robust L 1-norm distance to alleviate the negative effect of traffic data with outliers. Although …
Improved artificial rabbits optimization with ensemble learning-based traffic flow monitoring on intelligent transportation system
Traffic flow monitoring plays a crucial role in Intelligent Transportation Systems (ITS) by
dealing with real-time data on traffic situations and allowing effectual traffic management …
dealing with real-time data on traffic situations and allowing effectual traffic management …
Attention-based spatial–temporal adaptive dual-graph convolutional network for traffic flow forecasting
Accurate traffic flow forecasting (TFF) is a prerequisite for urban traffic control and guidance,
which has become the key to avoiding traffic congestion and improving traffic management …
which has become the key to avoiding traffic congestion and improving traffic management …
Urban traffic congestion level prediction using a fusion-based graph convolutional network
R Feng, H Cui, Q Feng, S Chen, X Gu… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
In an urban environment, the accurate prediction of congestion levels is a prerequisite for
formulating traffic demand management strategies reasonably. Current traffic forecasting …
formulating traffic demand management strategies reasonably. Current traffic forecasting …
Traffic speed forecasting for urban roads: A deep ensemble neural network model
Real-time and accurate traffic state forecasting of urban roads is of great significance to
improve traffic efficiency and optimize travel routes. However, future traffic state forecasting …
improve traffic efficiency and optimize travel routes. However, future traffic state forecasting …