Link traffic speed forecasting using convolutional attention-based gated recurrent unit

Khodabandelou, Ghazaleh; Kheriji, Walid; Selem, Fouad Hadj

doi:10.1007/s10489-020-02020-8

Cited by 33 publications

(9 citation statements)

References 42 publications

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“…Target detection is an important research direction in the field of computer vision, where the main task is to extract the coordinates and analyse the category of the target to be extracted in the picture. Target detection is the most fundamental part in the context of intelligent recognition and is the basis for other advanced computer vision tasks [13]. Target detection research has high use in the direction of computer vision intelligent security protection, humancomputer interaction, robot space exploration, and lowaltitude target tracking.…”

Section: Related Workmentioning

confidence: 99%

A Neural Network-Based Method for Fast Capture and Tracking of Laser Links between Nonorbiting Platforms

et al. 2022

Computational Intelligence and Neuroscience

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In this paper, a neural network approach is used to conduct an in-depth study and analysis of the fast capture tracking method for laser links between nonorbiting platforms. The experimental platform of the convolutional neural network- (CNN-) based free-space optical communication (FSO) wavefront correction system is built indoors, and the wavefront distortion correction performance of the CNN-based wavefront correction method is investigated. The experimental results show that the coupling power loss can be reduced to small after the CNN method correction under weak and strong turbulence. The accuracy of the above model is verified by comparing the simulation data with the experimentally measured data, thus realizing the coordinate decoupling of the coarse aiming mechanism and weakening the influence of structural factors on the tracking accuracy of the system. The tracking correlation equation of the influence of beam far-field dynamic characteristics on the tracking stability of the link is established, and the correlation factor variance of beam far-field dynamic characteristics is used to provide a quantitative analysis method for the evaluation and prediction of the comprehensive performance of the link tracking stability. The influence of beam divergence angle, wavefront distortion, detector accuracy, and atmospheric turbulence disturbance on the correlation factor variance of beam far-field dynamic characteristics of laser link beacons is modelled, and the link tracking stability optimization method is proposed under the requirement of link tracking accuracy, which provides an effective solution analysis method to realize the improvement of laser link tracking stability.

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Section: Related Workmentioning

confidence: 99%

A Neural Network-Based Method for Fast Capture and Tracking of Laser Links between Nonorbiting Platforms

et al. 2022

Computational Intelligence and Neuroscience

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“… LSTM Intra-urban Individual flows [ 30 ] TFG, RSB LSTM Intra-urban Road traffic [ 31 ] MPL, LBS, CDR GRU Intra-urban Individual flows [ 32 ] GPS traj. GRU Intra-urban Individual flows [ 33 ] ILD, GPS traj. ConvGRU Intra-urban Traffic speed [ 34 ] ILD, GPS traj.…”

Section: Related Workmentioning

confidence: 99%

Nation-wide human mobility prediction based on graph neural networks

Terroso-Sáenz

Muñoz

2021

Appl Intell

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Nowadays, the anticipation of human mobility flow has important applications in many domains ranging from urban planning to epidemiology. Because of the high predictability of human movements, numerous successful solutions to perform such forecasting have been proposed. However, most focus on predicting human displacements on an intra-urban spatial scale. This study proposes a predictor for nation-wide mobility that allows anticipating inter-urban displacements at larger spatial granularity. For this goal, a Graph Neural Network (GNN) was used to consider the latent relationships among large geographical regions. The solution has been evaluated with an open dataset including trips throughout the country of Spain and the current weather conditions. The results indicate a high accuracy in predicting the number of trips for multiple time horizons, and more important, they show that our proposal only needs a single model for processing all the mobility areas in the dataset, whereas other techniques require a different model for each area under study.

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“…Dai et al [47] combined the Spatio-temporal analysis with a GRU to predict traffic flow, the experimental results show that the prediction results of the GRU network are better than CNN and artificial neural networks ANN. Khodabandelou et al [48] fused convolutional attention and GRU network to build a short-term traffic flow prediction model, and proved the effectiveness of the model.…”

Section: Literature Reviewmentioning

confidence: 99%

Prediction model for short‐term traffic flow based on a K‐means‐gated recurrent unit combination

Sun

et al. 2022

IET Intelligent Trans Sys

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Short‐term forecasting of traffic flow is an indispensable part of easing traffic pressure. Considering that different traffic flow patterns will affect the short‐term traffic flow prediction results, a combined method based on the K‐means clustering algorithm and gated recurrent unit (GRU) is proposed to build a short‐term traffic flow prediction model to overcome the above problems. The K‐means algorithm is used to cluster historical traffic flow data to establish different traffic flow pattern libraries. The K‐nearest neighbour (KNN) classification algorithm is used to determine the historical traffic flow pattern most similar to the traffic flow change trend of the date to be predicted. All historical traffic flow data in this category is used training samples to make targeted predictions. The traffic flow data of performance measurement system (PeMS) in California, USA is used to verify the performance of the proposed model. Compared with the GRU network, stacked auto encoders (SAEs), random forest (RF), and support vector machine regression (SVR), the results show that the proposed combination model K‐means‐GRU considers the diversity of traffic flow patterns and improves the prediction accuracy, it can better solve the short‐term traffic flow prediction problem.

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Link traffic speed forecasting using convolutional attention-based gated recurrent unit

Cited by 33 publications

References 42 publications

A Neural Network-Based Method for Fast Capture and Tracking of Laser Links between Nonorbiting Platforms

A Neural Network-Based Method for Fast Capture and Tracking of Laser Links between Nonorbiting Platforms

Nation-wide human mobility prediction based on graph neural networks

Prediction model for short‐term traffic flow based on a K‐means‐gated recurrent unit combination

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