Traffic speed prediction under non-recurrent congestion: Based on LSTM method and BeiDou navigation satellite system data
J Zhao, Y Gao, Z Bai, H Wang… - IEEE Intelligent …, 2019 - ieeexplore.ieee.org
The full utilization of Location-Based Vehicle Sensor Data (LB-VSD) can improve the
efficiency of traffic control and management. Currently, LB-VSD is widely applied to the …
efficiency of traffic control and management. Currently, LB-VSD is widely applied to the …
Short-term traffic flow prediction for urban road sections based on time series analysis and LSTM_BILSTM method
C Ma, G Dai, J Zhou - IEEE Transactions on Intelligent …, 2021 - ieeexplore.ieee.org
The real-time performance and accuracy of traffic flow prediction directly affect the efficiency
of traffic flow guidance systems, and traffic flow prediction is a hotspot in the field of …
of traffic flow guidance systems, and traffic flow prediction is a hotspot in the field of …
Short-term traffic flow prediction: An integrated method of econometrics and hybrid deep learning
This study proposes a short-term traffic flow prediction framework. The vector autoregression
(VAR) model based on econometric theory and the CNN-LSTM hybrid neural network model …
(VAR) model based on econometric theory and the CNN-LSTM hybrid neural network model …
Traffic flow forecast through time series analysis based on deep learning
J Zheng, M Huang - IEEE Access, 2020 - ieeexplore.ieee.org
Traffic congestion is a thorny issue to many large and medium-sized cities, posing a serious
threat to sustainable urban development. Recently, intelligent traffic system (ITS) has …
threat to sustainable urban development. Recently, intelligent traffic system (ITS) has …
Short‐term traffic speed forecasting based on attention convolutional neural network for arterials
Q Liu, B Wang, Y Zhu - Computer‐Aided Civil and Infrastructure …, 2018 - Wiley Online Library
As an important part of the intelligent transportation system (ITS), short‐term traffic prediction
has become a hot research topic in the field of traffic engineering. In recent years, with the …
has become a hot research topic in the field of traffic engineering. In recent years, with the …
[HTML][HTML] An autoencoder and LSTM-based traffic flow prediction method
Smart cities can effectively improve the quality of urban life. Intelligent Transportation System
(ITS) is an important part of smart cities. The accurate and real-time prediction of traffic flow …
(ITS) is an important part of smart cities. The accurate and real-time prediction of traffic flow …
Using LSTM and GRU neural network methods for traffic flow prediction
R Fu, Z Zhang, L Li - 2016 31st Youth academic annual …, 2016 - ieeexplore.ieee.org
Accurate and real-time traffic flow prediction is important in Intelligent Transportation System
(ITS), especially for traffic control. Existing models such as ARMA, ARIMA are mainly linear …
(ITS), especially for traffic control. Existing models such as ARMA, ARIMA are mainly linear …
Short-term traffic flow prediction based on least square support vector machine with hybrid optimization algorithm
Accurate short-term traffic flow prediction plays an indispensable role for solving traffic
congestion. However, the structure of traffic data is nonlinear and complicated. It is a …
congestion. However, the structure of traffic data is nonlinear and complicated. It is a …
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) …
Data-driven short-term forecasting for urban road network traffic based on data processing and LSTM-RNN
W Xiangxue, X Lunhui, C Kaixun - Arabian Journal for Science and …, 2019 - Springer
A short-term traffic flow prediction framework is proposed for urban road networks based on
data-driven methods that mainly include two modules. The first module contains a set of …
data-driven methods that mainly include two modules. The first module contains a set of …