RETRACTED ARTICLE: Traffic identification and traffic analysis based on support vector machine

Zhu, Youchan; Zheng, Yi

doi:10.1007/s00521-019-04493-2

Cited by 23 publications

(8 citation statements)

References 11 publications

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“…(1) Preliminary construction of urban complex public space design evaluation index system: according to the design process of public space of urban complex, combined with relevant literature and design theory, after consulting the opinions of 30 experts and scholars who are familiar with and have participated in the design of public space of urban complex project, the evaluation index factors involved in the whole design process are summarized. Finally, 27 index factors in 6 aspects of urban complex project public space design scheme are obtained [24]. (2) Determine the final evaluation index system: after preprocessing each index data, the grey correlation analysis method is used to screen the indicators, and then the 18 indicators with the highest correlation with the evaluation results are obtained.…”

Section: Design Scheme Optimization Modelmentioning

confidence: 99%

Urban Complex Public Space Design Method Based on Support Vector Machine

Liu

2022

Mathematical Problems in Engineering

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Urban commercial complex is the product of social and economic development, as well as the inevitable trend of commercial development. The public space design of urban complex project is an important link in its development process, which is the link between the contact and the building, the city and the building, and the external image display of the complex. Therefore, choosing a suitable and excellent public space design scheme is of great significance to show the superiority of the complex project and improve the project satisfaction and the sustainable operation in the later stage. This paper first defines the concept of urban complex public space and focuses on the analysis of the urban complex public space from four aspects of constituent elements, functional classification, design theory, and principle. Then, the Delphi method is used to determine 26 evaluation indicators to evaluate the urban complex public space design scheme, and the relevant index data of 20 urban complex public space design schemes are collected. The grey correlation analysis method is used to analyze the correlation degree between each indicator and the scheme winning bid. The final evaluation system of urban complex public space design scheme is obtained after removing the 8 indicators with low correlation degree. Support vector mechanism was used to build the optimization model of urban complex public space design scheme, and its accuracy was verified, which provides a relatively simple and quantitative method for urban public space design in the future, helps to improve the city’s business structure, improves the business environment of the city, makes the commercial complex more effectively play its own characteristics and advantages, effectively stimulates the vitality of urban space, and creates more perfect architectural space and urban space for people.

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Section: Design Scheme Optimization Modelmentioning

confidence: 99%

Urban Complex Public Space Design Method Based on Support Vector Machine

Liu

2022

Mathematical Problems in Engineering

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“…They applied semi-supervised machine learning techniques, considering packet characteristics such as the unbalanced traffic distribution between classes. While the authors in [21] used SVM to classify the network traffic in campus backbone networks. In their study, SVM achieved reliable and accurate results using biased and unbiased test samples.…”

Section: Traffic Classification and Predictionmentioning

confidence: 99%

Machine Learning Models for Traffic Classification in Electromagnetic Nano-Networks

Galal

Hesselbach

2022

IEEE Access

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The number of nano-sensors connected to wireless electromagnetic nano-network generates different traffic volumes that have increased dramatically, enabling various applications of the Internet of nano-things. Nano-network traffic classification is more challenging nowadays to analyze different types of flows and study the overall performance of a nano-network that connects to the Internet through micro/nanogateways. There are traditional techniques to classify traffic, such as port-based technique and load-based technique, however the most promising technique used recently is machine learning. As machine learning models have a great impact on traffic classification and network performance evaluation in general, it is difficult to declare which is the best or the most suitable model to address the analysis of large volumes of traffic collected in operational nano-networks. In this paper, we study the classification problem of nano-network traffic captured by micro/nano-gateway, and then five supervised machine learning algorithms are used to analyze and classify the nano-network traffic from traditional traffic. Experimental analysis of the proposed models is evaluated and compared to show the most adequate classifier for nano-network traffic that gives very good accuracy and performance score to other classifiers.

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“…Traffic stream forecast and traffic state discrimination are two key problems in traffic guidance system. Providing RT and accurate traffic stream information and traffic state information can provide reference for travelers to make reasonable route selection [11]. Realizing fast and effective traffic state recognition is an important guarantee for realizing RT intelligent and effective control of urban traffic [12].…”

Section: Introductionmentioning

confidence: 99%

SVM-Based Real-Time Identification Model of Dangerous Traffic Stream State

Huang

2022

Wireless Communications and Mobile Computing

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By comparing and studying the correlation between traffic stream parameters and traffic safety of different highways, the correlations of traffic natural quantity, traffic equivalent, passenger-cargo ratio, car following percentage, congestion degree, and time occupancy rate are obtained. The traffic stream state before the actual accident is used as the criterion to judge the bad traffic stream state. The main parameters are obtained by extracting the parameters from the traffic stream data at the lane level and reducing the dimension of the parameters with the principal component analysis method. Establish a SVM model for RT early warning of traffic stream safety. Compared with other methods, the adaptive parameter selection method can adaptively select parameters according to the training sample set, realize the adaptive ability of the forecast model, and effectively improve the forecast accuracy of traffic stream. This paper studies the risk early warning model of road traffic accidents, which can transform the problem of road traffic safety into active early warning and improve the level of traffic safety. This study provides safety management measures for highway operation departments, which has certain theoretical significance and practical application value.

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RETRACTED ARTICLE: Traffic identification and traffic analysis based on support vector machine

Cited by 23 publications

References 11 publications

Urban Complex Public Space Design Method Based on Support Vector Machine

Urban Complex Public Space Design Method Based on Support Vector Machine

Machine Learning Models for Traffic Classification in Electromagnetic Nano-Networks

SVM-Based Real-Time Identification Model of Dangerous Traffic Stream State

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