Short‐term traffic flow prediction of road network based on deep learning

Han, Lei; Huang, Yeou‐Lih

doi:10.1049/iet-its.2019.0133

Cited by 38 publications

(16 citation statements)

References 31 publications

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“…e process of detecting wolves in the wolf pack algorithm can be considered as the process of approximating the performance of the feedforward neural network algorithm in the traditional wolf pack algorithm, but in the process of catching wolves to find different animals, the number of directions Y is different, and the value is randomly selected according to the actual situation when taking the value, and the value range is represented by [y min , y max ] [22]. However, the number of directions that wolves look for is not affected by changes in the number of walks or the number of algorithm iterations.…”

Section: Improved Wolf Pack Algorithmmentioning

confidence: 99%

Highway Traffic Flow Prediction Algorithm Based on Multiscale Transformation and Convolutional Networks

Luo

Wang

Ming

2022

Journal of Control Science and Engineering

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In order to solve the problem that the traditional long-term high-speed traffic forecasting algorithm is affected by the approximation ability of the function and easy to fall into the local mass value, we wrote a multivariate-based highway traffic forecasting algorithm scaling and convolutional networks. Because the feedforward wavelet neural network algorithm predicts the short-term traffic flow in different areas, it is necessary to examine the ability to predict the difference between different models. From the standard feedforward wavelet neural network algorithm using global optimization capabilities, we improve the wolf pack algorithm, improve the search accuracy of the algorithm, get the best solution of the estimated value of the work according to the search results when completing the research objectives, and get the ability to predict the work of the model. Feedforward neural network algorithm: we develop and obtain the best short-term high-speed traffic forecast values. The results are as follows: after using the author’s algorithm, the processing time increases by 1.5 seconds, but the average percentage of errors decreases by more than 50%, in fact the error and the root mean square error decreased by about 30%, and the smoothing coefficient increased by about 1%. The prediction of the author’s algorithm for short-term high-speed traffic is better than the wavelet neural network prediction algorithm, and the prediction accuracy and stability of the author’s algorithm are higher.

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Section: Improved Wolf Pack Algorithmmentioning

confidence: 99%

Highway Traffic Flow Prediction Algorithm Based on Multiscale Transformation and Convolutional Networks

Luo

Wang

Ming

2022

Journal of Control Science and Engineering

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“…It includes two important operations, namely convolution and pooling. 25 Convolution is a mathematical operator that combines two functions and generates the third function:…”

Section: Risk Assessment Methods Based On Deep Learningmentioning

confidence: 99%

The data flow risk monitoring system of the expressway networking system based on deep learning

Zhong

Ding

Zhang

et al. 2022

Int J of Intelligent Sys

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To ensure the reliability and safety of expressway networking systems, this paper designs a data flow risk monitoring system for expressway networking systems based on deep learning. The monitoring system is composed of data flow risk analysis, formulation of safety strategy, real-time monitoring, and disaster recovery. Data flow risk analysis is the basis for the operation of each part of the system. Meanwhile, indexes such as network management, data assets, and network resources are selected to build a data flow risk monitoring index system. The deep convolution neural network model is constructed, and the data flow risk monitoring index data are input into the deep convolution neural network to extract the index data features through the convolution and pooling process.Based on this, feature mapping is realized with a multilayer perceptron, and the index data risk classification results of data flow risk monitoring are output by the SoftMax classifier. The experimental resultsshow that the monitoring system can obtain accurate data flow risk analysis results which effectively reduces the data loss rate, alleviate the impact of different types of malicious attacks, and ensure the stability and security of the experimental object.

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“…The continuous improvement of machine learning and deep learning technology theory brings new prospects for the development of ITS. Deep learning networks are widely used in short-term traffic flow forecasting due to their strong nonlinear fitting capabilities [7][8][9]. Methods based on machine learning and deep learning can more effectively and accurately characterize the time-varying characteristics of traffic flow data.…”

Section: Introductionmentioning

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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Short‐term traffic flow prediction of road network based on deep learning

Cited by 38 publications

References 31 publications

Highway Traffic Flow Prediction Algorithm Based on Multiscale Transformation and Convolutional Networks

Highway Traffic Flow Prediction Algorithm Based on Multiscale Transformation and Convolutional Networks

The data flow risk monitoring system of the expressway networking system based on deep learning

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

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