T-LSTM: A long short-term memory neural network enhanced by temporal information for traffic flow prediction

L Mou, P Zhao, H Xie, Y Chen - Ieee Access, 2019 - ieeexplore.ieee.org
Short-term traffic flow prediction is one of the most important issues in the field of intelligent
transportation systems. It plays an important role in traffic information service and traffic …

Deep bi-directional long short-term memory model for short-term traffic flow prediction

J Wang, F Hu, L Li - … , ICONIP 2017, Guangzhou, China, November 14–18 …, 2017 - Springer
Short-term traffic flow prediction plays an important role in intelligent transportation system.
Numerous researchers have paid much attention to it in the past decades. However, 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 …

LSTM-based traffic flow prediction with missing data

Y Tian, K Zhang, J Li, X Lin, B Yang - Neurocomputing, 2018 - Elsevier
Traffic flow prediction plays a key role in intelligent transportation systems. However, since
traffic sensors are typically manually controlled, traffic flow data with varying length, irregular …

[HTML][HTML] Hybrid LSTM neural network for short-term traffic flow prediction

Y Xiao, Y Yin - Information, 2019 - mdpi.com
The existing short-term traffic flow prediction models fail to provide precise prediction results
and consider the impact of different traffic conditions on the prediction results in an actual …

A hybrid deep learning model with attention-based conv-LSTM networks for short-term traffic flow prediction

H Zheng, F Lin, X Feng, Y Chen - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Accurate short-time traffic flow prediction has gained gradually increasing importance for
traffic plan and management with the deployment of intelligent transportation systems (ITSs) …

Long-term traffic prediction based on lstm encoder-decoder architecture

Z Wang, X Su, Z Ding - IEEE Transactions on Intelligent …, 2020 - ieeexplore.ieee.org
Accurate traffic flow prediction is becoming increasingly important for transportation
planning, control, management, and information services of successful. Numerous existing …

[HTML][HTML] A combined deep learning method with attention-based LSTM model for short-term traffic speed forecasting

P Wu, Z Huang, Y Pian, L Xu, J Li… - Journal of Advanced …, 2020 - hindawi.com
Short-term traffic speed prediction is a promising research topic in intelligent transportation
systems (ITSs), which also plays an important role in the real-time decision-making of traffic …

Traffic flow prediction with long short-term memory networks (LSTMs)

H Shao, BH Soong - 2016 IEEE region 10 conference …, 2016 - ieeexplore.ieee.org
Accurate traffic flow information is crucial for an intelligent transportation system
management and deployment. Over the past few years, many existing models have been …

[HTML][HTML] An LSTM-based method with attention mechanism for travel time prediction

X Ran, Z Shan, Y Fang, C Lin - Sensors, 2019 - mdpi.com
Traffic prediction is based on modeling the complex non-linear spatiotemporal traffic
dynamics in road network. In recent years, Long Short-Term Memory has been applied to …