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 …
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 …
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 …
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 …
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 …
Forecasting the effect of traffic control strategies in railway systems: A hybrid machine learning method
Estimating the impacts of traffic control strategies (TCSs) can provide feedback in traffic
control and help to identify the effective ones among massive strategies, thus boosting …
control and help to identify the effective ones among massive strategies, thus boosting …
[HTML][HTML] Ship traffic flow prediction in wind farms water area based on spatiotemporal dependence
T Xu, Q Zhang - Journal of Marine Science and Engineering, 2022 - mdpi.com
To analyze the changing characteristics of ship traffic flow in wind farms water area, and to
improve the accuracy of ship traffic flow prediction, a Gated Recurrent Unit (GRU) of a …
improve the accuracy of ship traffic flow prediction, a Gated Recurrent Unit (GRU) of a …
Spatial-temporal traffic performance collaborative forecast in urban road network based on dynamic factor model
Many urban road networks today are experiencing increasing congestion that threatens not
only transport efficiency but also living environment. To solve these problems, providing …
only transport efficiency but also living environment. To solve these problems, providing …