Results 1 to 10 of about 24,574 (195)

Short-Term Traffic Flow Prediction Based on VMD and IDBO-LSTM

IEEE Access, 2023
To improve the accuracy of short term traffic flow prediction and to solve the problems of nonlinearity of short term traffic flow, more noise in the data, and more difficult to determine the parametes of long short term memory networks, a combined ...
Ke Zhao, Dudu Guo, Miao Sun, Chenao Zhao, Hongbo Shuai   +4 more
doaj   +3 more sources

A combined method for short-term traffic flow prediction based on recurrent neural network

Alexandria Engineering Journal, 2021
The accurate prediction of real-time traffic flow is indispensable to intelligent transport systems. However, the short-term prediction remains a thorny issue, due to the complexity and stochasticity of the traffic flow.
Saiqun Lu, Qiyan Zhang, Guangsen Chen, Dewen Seng   +3 more
doaj   +4 more sources

Deep learning for short-term traffic flow prediction [PDF]

Transportation Research Part C: Emerging Technologies, 2016
We develop a deep learning model to predict traffic flows. The main contribution is development of an architecture that combines a linear model that is fitted using l 1 regularization and a sequence of tanh layers.
Nicholas G. Polson, Vadim O. Sokolov
semanticscholar   +4 more sources

A Short-Term Traffic Flow Prediction Method Based on Personalized Lightweight Federated Learning. [PDF]

Sensors (Basel)
Traffic flow prediction can guide the rational layout of land use. Accurate traffic flow prediction can provide an important basis for urban expansion planning.
Dai G, Tang J.
europepmc   +2 more sources

A combined model for short-term traffic flow prediction based on variational modal decomposition and deep learning. [PDF]

Sci Rep
The emergence of Deep Learning provides an opportunity for traffic flow prediction. However, uncertainty and volatility exhibited by nonlinearity and instability of traffic flow pose challenges to Deep Learning models.
Ren C, Fu F, Yin C, Lu L, Cheng L.
europepmc   +2 more sources

A Hybrid Deep Learning Model With Attention-Based Conv-LSTM Networks for Short-Term Traffic Flow Prediction

IEEE Transactions on Intelligent Transportation Systems, 2021
Accurate short-time traffic flow prediction has gained gradually increasing importance for traffic plan and management with the deployment of intelligent transportation systems (ITSs).
Haifeng Zheng, Youjia Chen, Xinxin Feng
exaly   +2 more sources

Short-Term Traffic Flow Prediction Based on a K-Nearest Neighbor and Bidirectional Long Short-Term Memory Model

Applied Sciences, 2023
In the previous research on traffic flow prediction models, most of the models mainly studied the time series of traffic flow, and the spatial correlation of traffic flow was not fully considered.
Weiqing Zhuang, Yongbo Cao
doaj   +2 more sources

SE-MAConvLSTM: A deep learning framework for short-term traffic flow prediction combining Squeeze-and-Excitation Network and Multi-Attention Convolutional LSTM Network. [PDF]

PLoS One
Traffic flow prediction is an important part of transportation management and planning. For example, accurate demand prediction of taxis and online car-hailing can reduce the waste of resources caused by empty cars.
Zhu R, Tang J, He X, Zhou X, Huang X, Wu F, Chen S.   +6 more
europepmc   +2 more sources

Spatiotemporal information enhanced multi-feature short-term traffic flow prediction. [PDF]

PLoS One
Accurately predicting traffic flow is crucial for optimizing traffic conditions, reducing congestion, and improving travel efficiency. To explore spatiotemporal characteristics of traffic flow in depth, this study proposes the MFSTBiSGAT model.
Huang D, He J, Tu Y, Ye Z, Xie L.
europepmc   +2 more sources

T-LSTM: A Long Short-Term Memory Neural Network Enhanced by Temporal Information for Traffic Flow Prediction

IEEE Access, 2019
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 guidance.
Luntian Mou, Pengfei Zhao, Haitao Xie, Yanyan Chen   +3 more
doaj   +3 more sources