Multi-Dimensional Traffic Congestion Detection Based on Fusion of Visual Features and Convolutional Neural Network

Xiao, Ke; Shi, Ling‐Feng; Guo, Wenzhong; Chen, Dewang

doi:10.1109/tits.2018.2864612

Cited by 60 publications

(49 citation statements)

References 28 publications

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“…e GRU model cannot well handle all these kinds of high-dimensional data, the convolution neural network (CNN) [29] is ideal for processing high-dimensional data, which has been widely used in image recognition and the fields of prediction [30]. When there is a strong relationship between the nearby data point, CNN can capture local trend features and scale-invariant features [31,32]. In [33], the author proposed an end-to-end automatic image annotation method based on a deep CNN and multilabel data augmentation, and the model performs well in automatic image annotation.…”

Section: Introductionmentioning

confidence: 99%

A Short-Term Load Forecasting Method Based on GRU-CNN Hybrid Neural Network Model

Chun

Hao

et al. 2020

Mathematical Problems in Engineering

100

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Short-term load forecasting (STLF) plays a very important role in improving the economy and stability of the power system operation. With the smart meters and smart sensors widely deployed in the power system, a large amount of data was generated but not fully utilized, these data are complex and diverse, and most of the STLF methods cannot well handle such a huge, complex, and diverse data. For better accuracy of STLF, a GRU-CNN hybrid neural network model which combines the gated recurrent unit (GRU) and convolutional neural networks (CNN) was proposed; the feature vector of time sequence data is extracted by the GRU module, and the feature vector of other high-dimensional data is extracted by the CNN module. The proposed model was tested in a real-world experiment, and the mean absolute percentage error (MAPE) and the root mean square error (RMSE) of the GRU-CNN model are the lowest among BPNN, GRU, and CNN forecasting methods; the proposed GRU-CNN model can more fully use data and achieve more accurate short-term load forecasting.

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Section: Introductionmentioning

confidence: 99%

A Short-Term Load Forecasting Method Based on GRU-CNN Hybrid Neural Network Model

Chun

Hao

et al. 2020

Mathematical Problems in Engineering

100

View full text Add to dashboard Cite

show abstract

“…Therefore, ITS deployment is required to handle the enormous traffic efficiently, avoid congestion, reliability, and also provide the services to the passengers (like safety applications, emergency warnings, video streaming, lane change warning, and entertainment). These types of services, as mentioned before, need efficient and improved Packet Delivery ITS is a vital next-generation transportation system [3], [4], and it is a combination of communication technologies used in VANET management (i.e., efficiency, safety, and sustainability) and leading-edge information. In VANET, vehicles are like mobile nodes.…”

Section: Introductionmentioning

confidence: 99%

Software-Defined Networking (SDN) based VANET Architecture: Mitigation of Traffic Congestion

Adbeb¹,

Wu²

2020

IJACSA

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In VANETs (Vehicular Ad-hoc Networks), the number of vehicles increased continuously, leading to significant traffic problems like traffic congestion, a feasible path, and associated events like accidents. Though, the Intelligent Transportation System (ITS) providing excellent services, such as safety applications and emergency warnings. However, ITS has limitations regarding traffic management tasks, scalability, and flexibility because of the enormous number of vehicles. Therefore, extending the traditional VANET architecture is indeed a must. Thus, in the recent period, the design of the SD-VANETs (Software-Defined Networking defined VANETs) has gained significant interest and made VANET more intelligent. The SD-VANET architecture can handle the aforesaid VANET challenges. The centralized (logically) SDN architecture is programmable and also has global information about the VANET architecture. Therefore, it can effortlessly handle scalability, traffic management, and traffic congestion issues. The traffic congestion problem leads to longer trip times, decreases the vehicles' speed, and prolong average endto-end delay. Though, somewhere, some routes in the network are available with capacity, which can minimize the congestion problem and its characteristics. Therefore, we proposed heuristic algorithms called Congestion-Free Path (CFP) and Optimize CFP (OCFP), in SD-VANET architecture. The proposed algorithms address the traffic congestion issue and also provide a feasible path (less end-to-end delay) for a vehicle in VANET. We used the NS-3 simulator to evaluate the performance of the proposed algorithms, and for generating a real scenario of VANET traffic; we use the SUMO module. The results show that the proposed algorithms decrease road traffic congestion drastically compared to exiting approaches.

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“…Some techniques have been implemented in the embedded systems and applied to industry 4.0 applications, industrial electronics applications, consumer electronics applications, and other electronics applications. For instance, supervised learning techniques, including neural networks (NN) [9][10][11][12][13][14][15][16][17][18][19], convolutional neural networks (CNN) [20][21][22][23][24][25][26], and recurrent neural networks (RNN) [27][28][29][30][31][32], can be adopted for prediction applications and classification applications in the electronics industries. Unsupervised learning techniques, including restricted Boltzmann machine (RBM) [33,34], deep belief networks (DBN) [35], deep Boltzmann machine (DBM) [36], auto-encoders (AE) [37,38], and denoising auto-encoders (DAE) [39], can be used for denoising and generalization.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Applications with Practical Measured Results in Electronics Industries

et al. 2020

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This editorial introduces the Special Issue, entitled “Deep Learning Applications with Practical Measured Results in Electronics Industries”, of Electronics. Topics covered in this issue include four main parts: (I) environmental information analyses and predictions, (II) unmanned aerial vehicle (UAV) and object tracking applications, (III) measurement and denoising techniques, and (IV) recommendation systems and education systems. Four papers on environmental information analyses and predictions are as follows: (1) “A Data-Driven Short-Term Forecasting Model for Offshore Wind Speed Prediction Based on Computational Intelligence” by Panapakidis et al.; (2) “Multivariate Temporal Convolutional Network: A Deep Neural Networks Approach for Multivariate Time Series Forecasting” by Wan et al.; (3) “Modeling and Analysis of Adaptive Temperature Compensation for Humidity Sensors” by Xu et al.; (4) “An Image Compression Method for Video Surveillance System in Underground Mines Based on Residual Networks and Discrete Wavelet Transform” by Zhang et al. Three papers on UAV and object tracking applications are as follows: (1) “Trajectory Planning Algorithm of UAV Based on System Positioning Accuracy Constraints” by Zhou et al.; (2) “OTL-Classifier: Towards Imaging Processing for Future Unmanned Overhead Transmission Line Maintenance” by Zhang et al.; (3) “Model Update Strategies about Object Tracking: A State of the Art Review” by Wang et al. Five papers on measurement and denoising techniques are as follows: (1) “Characterization and Correction of the Geometric Errors in Using Confocal Microscope for Extended Topography Measurement. Part I: Models, Algorithms Development and Validation” by Wang et al.; (2) “Characterization and Correction of the Geometric Errors Using a Confocal Microscope for Extended Topography Measurement, Part II: Experimental Study and Uncertainty Evaluation” by Wang et al.; (3) “Deep Transfer HSI Classification Method Based on Information Measure and Optimal Neighborhood Noise Reduction” by Lin et al.; (4) “Quality Assessment of Tire Shearography Images via Ensemble Hybrid Faster Region-Based ConvNets” by Chang et al.; (5) “High-Resolution Image Inpainting Based on Multi-Scale Neural Network” by Sun et al. Two papers on recommendation systems and education systems are as follows: (1) “Deep Learning-Enhanced Framework for Performance Evaluation of a Recommending Interface with Varied Recommendation Position and Intensity Based on Eye-Tracking Equipment Data Processing” by Sulikowski et al. and (2) “Generative Adversarial Network Based Neural Audio Caption Model for Oral Evaluation” by Zhang et al.

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Multi-Dimensional Traffic Congestion Detection Based on Fusion of Visual Features and Convolutional Neural Network

Cited by 60 publications

References 28 publications

A Short-Term Load Forecasting Method Based on GRU-CNN Hybrid Neural Network Model

A Short-Term Load Forecasting Method Based on GRU-CNN Hybrid Neural Network Model

Software-Defined Networking (SDN) based VANET Architecture: Mitigation of Traffic Congestion

Deep Learning Applications with Practical Measured Results in Electronics Industries

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