LSTM network: a deep learning approach for short‐term traffic forecast
Short‐term traffic forecast is one of the essential issues in intelligent transportation system.
Accurate forecast result enables commuters make appropriate travel modes, travel routes …
Accurate forecast result enables commuters make appropriate travel modes, travel routes …
A learning-based multimodel integrated framework for dynamic traffic flow forecasting
Accurate and timely traffic flow forecasting is essential for many intelligent transportation
systems. However, it is quite challenging to develop an efficient and robust forecasting …
systems. However, it is quite challenging to develop an efficient and robust forecasting …
Short-term traffic flow forecasting with spatial-temporal correlation in a hybrid deep learning framework
Deep learning approaches have reached a celebrity status in artificial intelligence field, its
success have mostly relied on Convolutional Networks (CNN) and Recurrent Networks. By …
success have mostly relied on Convolutional Networks (CNN) and Recurrent Networks. By …
Traffic forecasting using least squares support vector machines
Y Zhang, Y Liu - Transportmetrica, 2009 - Taylor & Francis
Accurate and timely forecasting of traffic parameters is crucial for effective management of
intelligent transportation systems. Travel time index (TTI) is a fundamental measure in …
intelligent transportation systems. Travel time index (TTI) is a fundamental measure in …
SVRGSA: a hybrid learning based model for short‐term traffic flow forecasting
L Cai, Q Chen, W Cai, X Xu, T Zhou… - IET intelligent transport …, 2019 - Wiley Online Library
Accurate and timely short‐term traffic flow forecasting is a critical component for intelligent
transportation systems. However, it is quite challenging to develop an efficient and robust …
transportation systems. However, it is quite challenging to develop an efficient and robust …
Short-term traffic flow prediction using the modified elman recurrent neural network optimized through a genetic algorithm
A Sadeghi-Niaraki, P Mirshafiei, M Shakeri… - IEEE …, 2020 - ieeexplore.ieee.org
Traffic stream determining is an essential part of the intelligent transportation management
system. Precise prediction of traffic flow provides a basis for other tasks, like forecasting …
system. Precise prediction of traffic flow provides a basis for other tasks, like forecasting …
A graph CNN-LSTM neural network for short and long-term traffic forecasting based on trajectory data
T Bogaerts, AD Masegosa, JS Angarita-Zapata… - … Research Part C …, 2020 - Elsevier
Traffic forecasting is an important research area in Intelligent Transportation Systems that is
focused on anticipating traffic in order to mitigate congestion. In this work we propose a deep …
focused on anticipating traffic in order to mitigate congestion. In this work we propose a deep …
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 …
Error-distribution-free kernel extreme learning machine for traffic flow forecasting
Traffic flow modeling plays a crucial role in intelligent transportation systems, which is of vital
significance for mitigating traffic congestion and reducing carbon emissions. Owing to the …
significance for mitigating traffic congestion and reducing carbon emissions. Owing to the …
A distributed WND-LSTM model on MapReduce for short-term traffic flow prediction
Building data-driven intelligent transportation is a significant task for establishing data-
centric smart cities, and exceptionally efficient and accurate traffic flow prediction (TFP) is a …
centric smart cities, and exceptionally efficient and accurate traffic flow prediction (TFP) is a …