Deep irregular convolutional residual LSTM for urban traffic passenger flows prediction
Urban traffic passenger flows prediction is practically important to facilitate many real
applications including transportation management and public safety. Recently, deep …
applications including transportation management and public safety. Recently, deep …
Dynamic spatial-temporal representation learning for traffic flow prediction
As a crucial component in intelligent transportation systems, traffic flow prediction has
recently attracted widespread research interest in the field of artificial intelligence (AI) with …
recently attracted widespread research interest in the field of artificial intelligence (AI) with …
Short-term prediction of bus passenger flow based on a hybrid optimized LSTM network
Y Han, C Wang, Y Ren, S Wang, H Zheng… - … International Journal of …, 2019 - mdpi.com
The accurate prediction of bus passenger flow is the key to public transport management
and the smart city. A long short-term memory network, a deep learning method for modeling …
and the smart city. A long short-term memory network, a deep learning method for modeling …
Spatial temporal incidence dynamic graph neural networks for traffic flow forecasting
Accurate and real-time traffic passenger flows forecasting at transportation hubs, such as
subway/bus stations, is a practical application and of great significance for urban traffic …
subway/bus stations, is a practical application and of great significance for urban traffic …
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) …
A novel passenger flow prediction model using deep learning methods
L Liu, RC Chen - Transportation Research Part C: Emerging …, 2017 - Elsevier
Currently, deep learning has been successfully applied in many fields and achieved
amazing results. Meanwhile, big data has revolutionized the transportation industry over the …
amazing results. Meanwhile, big data has revolutionized the transportation industry over the …
Deep learning architecture for short-term passenger flow forecasting in urban rail transit
Short-term passenger flow forecasting is an essential component in urban rail transit
operation. Emerging deep learning models provide good insight into improving prediction …
operation. Emerging deep learning models provide good insight into improving prediction …
Leveraging spatio-temporal patterns for predicting citywide traffic crowd flows using deep hybrid neural networks
Predicting the accurate traffic crowd flows is of practical importance for intelligent
transportation systems (ITS). However, it is challenging because traffic flows are affected by …
transportation systems (ITS). However, it is challenging because traffic flows are affected by …
Exploiting dynamic spatio-temporal correlations for citywide traffic flow prediction using attention based neural networks
For intelligent transportation systems (ITS), predicting urban traffic crowd flows is of great
importance. However, it is challenging to represent various complex spatial relationships …
importance. However, it is challenging to represent various complex spatial relationships …
Deep spatio-temporal 3D densenet with multiscale ConvLSTM-Resnet network for citywide traffic flow forecasting
R He, Y Liu, Y Xiao, X Lu, S Zhang - Knowledge-Based Systems, 2022 - Elsevier
Reliable traffic flow forecasting is paramount in Intelligent Transportation Systems (ITS) as it
can effectively improve traffic efficiency and social security. Its vital challenge is to effectively …
can effectively improve traffic efficiency and social security. Its vital challenge is to effectively …