Survey on traffic prediction in smart cities
The rapid development in machine learning and in the emergence of new data sources
makes it possible to examine and predict traffic conditions in smart cities more accurately …
makes it possible to examine and predict traffic conditions in smart cities more accurately …
Deep learning models for traffic flow prediction in autonomous vehicles: A review, solutions, and challenges
In the last few years, there has been an exponential increase in the usage of the
autonomous vehicles across the globe. It is due to an exponential increase in the popularity …
autonomous vehicles across the globe. It is due to an exponential increase in the popularity …
Privacy-preserving traffic flow prediction: A federated learning approach
Existing traffic flow forecasting approaches by deep learning models achieve excellent
success based on a large volume of data sets gathered by governments and organizations …
success based on a large volume of data sets gathered by governments and organizations …
Short-term traffic flow prediction based on optimized deep learning neural network: PSO-Bi-LSTM
Traffic flow prediction is important for urban planning and traffic congestion alleviation as
well as for intelligent traffic management systems. Due to the periodic characteristics and …
well as for intelligent traffic management systems. Due to the periodic characteristics and …
T-GCN: A temporal graph convolutional network for traffic prediction
Accurate and real-time traffic forecasting plays an important role in the intelligent traffic
system and is of great significance for urban traffic planning, traffic management, and traffic …
system and is of great significance for urban traffic planning, traffic management, and 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 hybrid deep learning model with 1DCNN-LSTM-Attention networks for short-term traffic flow prediction
K Wang, C Ma, Y Qiao, X Lu, W Hao, S Dong - Physica A: Statistical …, 2021 - Elsevier
With the rapid development of social economy, the traffic volume of urban roads has raised
significantly, which has led to increasingly serious urban traffic congestion problems, and …
significantly, which has led to increasingly serious urban traffic congestion problems, and …
LSTM-based traffic flow prediction with missing data
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 …
traffic sensors are typically manually controlled, traffic flow data with varying length, irregular …
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 …
Learning traffic as images: A deep convolutional neural network for large-scale transportation network speed prediction
This paper proposes a convolutional neural network (CNN)-based method that learns traffic
as images and predicts large-scale, network-wide traffic speed with a high accuracy …
as images and predicts large-scale, network-wide traffic speed with a high accuracy …