Traffic flow prediction using LSTM with feature enhancement
Long short-term memory (LSTM) is widely used to process and predict events with time
series, but it is difficult to solve exceedingly long-term dependencies, possibly because the …
series, but it is difficult to solve exceedingly long-term dependencies, possibly because the …
Data-driven remaining useful life prediction via multiple sensor signals and deep long short-term memory neural network
Remaining useful life (RUL) prediction is very important for improving the availability of a
system and reducing its life cycle cost. This paper proposes a deep long short-term memory …
system and reducing its life cycle cost. This paper proposes a deep long short-term memory …
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 …
Short-term traffic state prediction from latent structures: Accuracy vs. efficiency
Recently, deep learning models have shown promising performances in many research
areas, including traffic states prediction, due to their ability to model complex nonlinear …
areas, including traffic states prediction, due to their ability to model complex nonlinear …
Predicting cycle-level traffic movements at signalized intersections using machine learning models
Predicting accurate traffic parameters is fundamental and cost-effective in providing traffic
applications with required information. Many studies adopted various parametric and …
applications with required information. Many studies adopted various parametric and …
Traffic congestion prediction based on GPS trajectory data
S Sun, J Chen, J Sun - International Journal of Distributed …, 2019 - journals.sagepub.com
Since speed sensors are not as widely used as GPS devices, the traffic congestion level is
predicted based on processed GPS trajectory data in this article. Hidden Markov model is …
predicted based on processed GPS trajectory data in this article. Hidden Markov model is …
SW-BiLSTM: a Spark-based weighted BiLSTM model for traffic flow forecasting
Accurate traffic flow forecasting (TFF) is significant for mitigating traffic congestion. To
address the existing issues of calculation and storage in dealing with big traffic flow data …
address the existing issues of calculation and storage in dealing with big traffic flow data …
Attention-based spatial–temporal adaptive dual-graph convolutional network for traffic flow forecasting
Accurate traffic flow forecasting (TFF) is a prerequisite for urban traffic control and guidance,
which has become the key to avoiding traffic congestion and improving traffic management …
which has become the key to avoiding traffic congestion and improving traffic management …
Traffic congestion prediction using decision tree, logistic regression and neural networks
Traffic congestion is a serious problem around the world and to a great extent influences
urban communities in various manners including increased stress levels, delayed deliveries …
urban communities in various manners including increased stress levels, delayed deliveries …
Estimating cycle-level real-time traffic movements at signalized intersections
N Mahmoud, M Abdel-Aty, Q Cai… - Journal of intelligent …, 2022 - Taylor & Francis
Real-time traffic movements at intersections is vital for transportation and traffic engineering.
It helps in providing intersection traffic data and optimizing signal control plans. This study …
It helps in providing intersection traffic data and optimizing signal control plans. This study …