Using support vector regression and K-nearest neighbors for short-term traffic flow prediction based on maximal information coefficient
G Lin, A Lin, D Gu - Information Sciences, 2022 - Elsevier
The prediction of short-term traffic flow is critical for improving service levels for drivers and
passengers as well as enhancing the efficiency of traffic management in the urban …
passengers as well as enhancing the efficiency of traffic management in the urban …
Deep belief network-based support vector regression method for traffic flow forecasting
H Xu, C Jiang - Neural Computing and Applications, 2020 - Springer
Instability is a common problem in deep belief network–back propagation forecasting model,
and the trend of traffic data will affect the forecasting results of the model. Therefore, this …
and the trend of traffic data will affect the forecasting results of the model. Therefore, this …
Transferability improvement in short-term traffic prediction using stacked LSTM network
Short-term traffic flow forecasting is a key element in Intelligent Transport Systems (ITS) to
provide proactive traffic state information to road network operators. A variety of methods to …
provide proactive traffic state information to road network operators. A variety of methods to …
An improved Bayesian combination model for short-term traffic prediction with deep learning
Short-term traffic volume prediction, which can assist road users in choosing appropriate
routes and reducing travel time cost, is a significant topic of intelligent transportation system …
routes and reducing travel time cost, is a significant topic of intelligent transportation system …
Prediction of traffic congestion based on LSTM through correction of missing temporal and spatial data
With the rapid increase in vehicle use during the fourth Industrial Revolution, road resources
have reached their supply limit. Active studies have therefore been conducted on intelligent …
have reached their supply limit. Active studies have therefore been conducted on intelligent …
Hierarchical traffic flow prediction based on spatial-temporal graph convolutional network
In recent years, traffic flow prediction has attracted more and more interest from both
academia and industry since such information can provide effective guidance for traffic …
academia and industry since such information can provide effective guidance for traffic …
Citywide traffic flow prediction based on multiple gated spatio-temporal convolutional neural networks
Traffic flow prediction is crucial for public safety and traffic management, and remains a big
challenge because of many complicated factors, eg, multiple spatio-temporal dependencies …
challenge because of many complicated factors, eg, multiple spatio-temporal dependencies …
Traffic flow prediction on urban road network based on license plate recognition data: combining attention-LSTM with genetic algorithm
Exploring traffic flow characteristics and predicting its variation patterns are the basis of
Intelligent Transportation Systems. The intermittent characteristics and intense fluctuation on …
Intelligent Transportation Systems. The intermittent characteristics and intense fluctuation on …
Exploiting dynamic spatio-temporal graph convolutional neural networks for citywide traffic flows prediction
The prediction of crowd flows is an important urban computing issue whose purpose is to
predict the future number of incoming and outgoing people in regions. Measuring the …
predict the future number of incoming and outgoing people in regions. Measuring the …
High-order Gaussian process dynamical models for traffic flow prediction
Traffic flow prediction, which predicts the future flow using historic flows, is an important task
in intelligent transportation systems (ITS). Efficient and accurate models for traffic flow …
in intelligent transportation systems (ITS). Efficient and accurate models for traffic flow …