Survey of neural network‐based models for short‐term traffic state prediction
LNN Do, N Taherifar, HL Vu - Wiley Interdisciplinary Reviews …, 2019 - Wiley Online Library
Traffic state prediction is a key component in intelligent transport systems (ITS) and has
attracted much attention over the last few decades. Advances in computational power and …
attracted much attention over the last few decades. Advances in computational power and …
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 …
Short-term forecasting of passenger demand under on-demand ride services: A spatio-temporal deep learning approach
Short-term passenger demand forecasting is of great importance to the on-demand ride
service platform, which can incentivize vacant cars moving from over-supply regions to over …
service platform, which can incentivize vacant cars moving from over-supply regions to over …
[HTML][HTML] Long-term traffic flow forecasting using a hybrid CNN-BiLSTM model
The increase of road traffic in large cities during the last years has produced that long and
short-term traffic flow forecasting is a critical need for the authorities. The availability of good …
short-term traffic flow forecasting is a critical need for the authorities. The availability of good …
An effective spatial-temporal attention based neural network for traffic flow prediction
Due to its importance in Intelligent Transport Systems (ITS), traffic flow prediction has been
the focus of many studies in the last few decades. Existing traffic flow prediction models …
the focus of many studies in the last few decades. Existing traffic flow prediction models …
A short-term traffic flow prediction model based on an improved gate recurrent unit neural network
W Shu, K Cai, NN Xiong - IEEE Transactions on Intelligent …, 2021 - ieeexplore.ieee.org
With the increasing demand for intelligent transportation systems, short-term traffic flow
prediction has become an important research direction. The memory unit of a Long Short …
prediction has become an important research direction. The memory unit of a Long Short …
Evaluation of spatio-temporal forecasting methods in various smart city applications
A Tascikaraoglu - Renewable and Sustainable Energy Reviews, 2018 - Elsevier
Together with the increasing population and urbanization, cities have started to face
challenges that hinder their socio-economic and sustainable development. The concept of …
challenges that hinder their socio-economic and sustainable development. The concept of …
Enhancing traffic intelligence in smart cities using sustainable deep radial function
AG Ismaeel, J Mary, A Chelliah, J Logeshwaran… - Sustainability, 2023 - mdpi.com
Smart cities have revolutionized urban living by incorporating sophisticated technologies to
optimize various aspects of urban infrastructure, such as transportation systems. Effective …
optimize various aspects of urban infrastructure, such as transportation systems. Effective …
Optimized structure of the traffic flow forecasting model with a deep learning approach
Forecasting accuracy is an important issue for successful intelligent traffic management,
especially in the domain of traffic efficiency and congestion reduction. The dawning of the …
especially in the domain of traffic efficiency and congestion reduction. The dawning of the …
Attention meets long short-term memory: A deep learning network for traffic flow forecasting
W Fang, W Zhuo, J Yan, Y Song, D Jiang… - Physica A: Statistical …, 2022 - Elsevier
Accurate forecasting of future traffic flow has a wide range of applications, which is a
fundamental component of intelligent transportation systems. However, timely and accurate …
fundamental component of intelligent transportation systems. However, timely and accurate …