An intelligent urban traffic data fusion analysis method based on improved artificial neural network

Liu, Xiaodan; Li, Chunliang

doi:10.3233/jifs-179273

Cited by 6 publications

(6 citation statements)

References 22 publications

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“…e dataset in this article is obtained by collating data from our own existing data and related articles. e sample dataset was divided into training, validation, and test samples in the ratio of 1 : 1: : 1 using the stratified sampling method, and the samples were normalized separately for the divided samples; the number of n 1) t , x i(2) t , . .…”

Section: Neural Network Fusion Bionic Algorithm Designmentioning

confidence: 99%

“…Each neuron represents a specific output function, and each connection between two neurons represents a weighted value of the signal passing through the connection, and the output of the network varies depending on the connection, weight, and output function of the network. From the perspective of global economic development, economic restructuring and upgrading have become the focus of attention of policymakers and the public in various countries [1]. e current international financial crisis has fully exposed the long-standing structural contradictions of the world economy, including the hollowing out of industries and the bubble of a virtual economy in developed countries, the imbalance between investment and consumption, and the imbalance between external and domestic demand in developing countries [2].…”

Section: Introductionmentioning

confidence: 99%

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Economic Structure Analysis Based on Neural Network and Bionic Algorithm

Dai

Lin

2021

Complexity

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In this article, an in-depth study and analysis of economic structure are carried out using a neural network fusion release algorithm. The method system defines the weight space and structure space of neural networks from the perspective of optimization theory, proposes a bionic optimization algorithm under the weight space and structure space, and establishes a neuroevolutionary method with shallow neural network and deep neural network as the research objects. In the shallow neuroevolutionary, the improved genetic algorithm (IGA) based on elite heuristic operation and migration strategy and the improved coyote optimization algorithm (ICOA) based on adaptive influence weights are proposed, and the shallow neuroevolutionary method based on IGA and the shallow neuroevolutionary method based on ICOA are applied to the weight space of backpropagation (BP) neural networks. In deep neuroevolutionary method, the structure space of convolutional neural network is proposed to solve the search space design of neural structure search (NAS), and the GA-based deep neuroevolutionary method under the structure space of convolutional neural network is proposed to solve the problem that numerous hyperparameters and network structure parameters can produce explosive combinations when designing deep learning models. The neural network fusion bionic algorithm used has the application value of exploring the spatial structure and dynamics of the socioeconomic system, improving the perception of the socioeconomic situation, and understanding the development law of society, etc. The idea is also verifiable through the present computer technology.

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Section: Neural Network Fusion Bionic Algorithm Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Economic Structure Analysis Based on Neural Network and Bionic Algorithm

Dai

Lin

2021

Complexity

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“…Urban planning data has both geographic data and characteristic data in the planning field. Multisource heterogeneity constitutes the main characteristics of urban planning data [16,17]. Digital city planning is a digital method to achieve the effective allocation and rational arrangement of urban space resources.…”

Section: Introductionmentioning

confidence: 99%

Characteristic Analysis of Multisource Heterogeneous Data in Digital City Planning Based on Internet of Things

Xiao

2021

Mobile Information Systems

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The purpose of this article is to use the Internet of Things related technology to analyze the characteristics of multisource and easy-to-purchase data for the different types of planning data and different levels of cognitive needs of participants in the entire urban planning process. This paper uses the ontology idea to reconstruct the relationship between multisource and heterogeneous planning data including Internet of Things data, planning documents, and planning drawings, to design the data semantic relationship of the ontology model elements, define the relationship between the data types, and implement the ontology-based method. The semantic expression algorithm in the planning field facilitates the exchange of various planning participants’ understanding of the planning scheme, at the same time, according to the classification of multisource heterogeneous data features, logical reasoning of ontology relationships, filtering redundant information, and multisource heterogeneous planning data visualization. Finally, the information of the same nature collected by the sensor nodes of the Internet of Things is batched, and the calculated fusion information is closer to the true value through a series of weighting formulas. Experiments prove that the feature analysis method proposed in this paper can maintain a loss of 0.02% and achieve an accuracy rate of 79.1% when the overall characteristics of digital city planning are reduced by 67%, which effectively proves the multisource heterogeneous data feature analysis for digital city planning importance.

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“…The number of private car ownership increases greatly and continuously, which leads to many traffic problems. Some experts have already conducted a series of studies on traffic problems [1][2][3]. Among them, the traffic congestion is a very serious traffic problem, which has caught the attention of the public and government departments.…”

Section: Introductionmentioning

confidence: 99%

Application of Quantum Genetic Optimization of LVQ Neural Network in Smart City Traffic Network Prediction

Zhang

Wang

et al. 2020

IEEE Access

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Accurate prediction of traffic flow in urban networks is of great significance for smart city management. A short-term traffic flow prediction algorithm of Quantum Genetic Algorithm-Learning Vector Quantization (QGA-LVQ) neural network is proposed to forecast the changes of traffic flow. Different from BP neural network, Learning Vector Quantization (LVQ) neural network is of simple structure, easy implementation and better clustering effect. Utilizing the global optimization ability of Quantum Genetic Algorithm (QGA), it is combined with LVQ neural network to overcome some shortcomings of LVQ neural network, including sensitive to initial weights and prone to local minima. In order to test the convergence ability and the timeliness of QGA-LVQ neural network in short-term traffic flow, some contrast experiments are performed. Experimental simulation results show that, QGA-LVQ neural network obtains excellent prediction results in prediction accuracy and convergence speed. Besides, compared with GA-BP neural network and wavelet neural network, QGA-LVQ neural network performs better in short-term traffic flow prediction. INDEX TERMS QGA, LVQ neural network, short-term traffic flow prediction, global optimization.

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An intelligent urban traffic data fusion analysis method based on improved artificial neural network

Cited by 6 publications

References 22 publications

Economic Structure Analysis Based on Neural Network and Bionic Algorithm

Economic Structure Analysis Based on Neural Network and Bionic Algorithm

Characteristic Analysis of Multisource Heterogeneous Data in Digital City Planning Based on Internet of Things

Application of Quantum Genetic Optimization of LVQ Neural Network in Smart City Traffic Network Prediction

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