Prediction of Entering Percentage into Expressway Service Areas Based on Wavelet Neural Networks and Genetic Algorithms

Shen, Xiaoyan; Zhang, Fan; Lv, Huitao; Liu, Jia; Liu, Haoxue

doi:10.1109/access.2019.2913177

Cited by 19 publications

(13 citation statements)

References 15 publications

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“…In Figs. 7-16, we compare ELM with wavelet neural network [31], SVM [32], LWLR [33], and BP neural network [34] methods. The parameters for five different methods are given in Table 4.…”

Section: Numerical Resultsmentioning

confidence: 99%

OP Performance Prediction for Complex Mobile Multiuser Networks Based on Extreme Learning Machine

Wang

Tao

et al. 2020

IEEE Access

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Due to the complex and variable environments of mobile communication, the mobile multiuser networks become a hot topic. To process active complex event in mobile multiuser networks, it is important to predict the system performance. In this work, the authors consider the multiuser networks which utilizes transmit antenna selection (TAS). We derive novel closed-form expressions for the outage probability (OP) in terms of the Meijer's G-function. Then, a extreme learning machine (ELM)-based OP performance prediction algorithm is proposed. We use the theoretical results to generate training data. We test back-propagation (BP) neural network, locally weighted linear regression (LWLR), wavelet neural network, ELM, and support vector machine (SVM) methods. Compared with wavelet neural network, SVM, BP neural network, and LWLR methods, the Monte-Carlo results shows that the proposed prediction algorithm can consistently achieve higher OP performance prediction results. INDEX TERMS Extreme learning machine, multiuser diversity, outage probability, performance prediction.

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“…In Figs. 7-16, we compare ELM with wavelet neural network [31], SVM [32], LWLR [33], and BP neural network [34] methods. The parameters for five different methods are given in Table 4.…”

Section: Numerical Resultsmentioning

confidence: 99%

OP Performance Prediction for Complex Mobile Multiuser Networks Based on Extreme Learning Machine

Wang

Tao

et al. 2020

IEEE Access

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“…By analyzing the operating data of the expressway service area, Wang et al ( 31 ) predicted the transient population in the service area of the expressway using the Long Short-Term Memory method. Shen et al ( 32 ) provided a theoretical basis for layout optimization and evaluation for the expressway service areas based on the economic and social adaptability. Zhao et al ( 33 ) developed a traffic flow prediction model to improve the expressway service area management ability.…”

Section: Introductionmentioning

confidence: 99%

Safety-oriented planning of expressway truck service areas based on driver demand

Ding

Wang

Chu

et al. 2022

Front. Public Health

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The rapid development of the economy has promoted the growth of freight transportation. The truck service areas on expressways, as the main places for truck drivers to rest, play an important role in ensuring the driving safety of trucks. If these service areas are constructed densely or provide a plentiful supply of parking areas, they are costly to construct. However, if the distance between two adjacent truck service areas is very large or the number of truck parking spaces in service areas is small, the supply will fail to meet the parking needs of truck drivers. In this situation, the continuous working time of truck drivers will be longer, and this is likely to cause driver fatigue and even traffic accidents. To address these issues, this paper established a non-linear optimization model for truck service area planning of expressways to optimize truck driving safety. An improved genetic algorithm is proposed to solve the model. A case study of a 215.5-kilometers-length section of the Guang-Kun expressway in China was used to demonstrate the effectiveness of the model and algorithm. As validated by this specific case, the proposed model and solution algorithm can provide an optimal plan for the layout of truck service areas that meet the parking needs of truck drivers while minimizing the service loss rate. The research results of this paper can contribute to the construction of truck service areas and the parking management of trucks on expressways.

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“…Furthermore, the CRITIC method [3] is used to classify the early warning level of the network public opinion. Finally, combining Genetic Algorithms (GA) and back-propagation neural network (BP), an GA-BP model (e.g., [4] , [17] , [13] , [23] ) is established to predict the level of the network public opinion. The internet public opinion data of COVID-19 pandemic after processing are chosen to train the network model.…”

Section: Introductionmentioning

confidence: 99%

Research on the Early-Warning Model of Network Public Opinion of Major Emergencies

2021

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The rapid development of Internet in recent years has led to a proliferation of social media networks as people who can gather online to share information, knowledge, and opinions. However, the network public opinion tends to generate strongly misleading and a large number of messages can cause shocks to the public once major emergencies appear. Therefore, we need to make correct prediction regarding and timely identify a potential crisis in the early warning of network public opinion. In view of this, this study fully considers the features of development and the propagation characteristics, so as to construct a network public opinion early warning index system that includes 4 first-level indicators and 13 second-level indicators. The weight of each indicator is calculated by the "CRITIC" method, so that the comprehensive evaluation value of each time point can be obtained and the early warning level of internet public opinion can be divided. Then, the Back Propagation neural network based on Genetic Algorithm (GA-BP) is used to establish a network public opinion early warning model. Finally, the major public health emergency, COVID-19 pandemic, is taken as a case for empirical analysis. The results show that by comparing with the traditional classification methods, such as BP neural network, decision tree, random forest, support vector machine and naive Bayes, GA-BP neural network has a higher accuracy rate for early warning of network public opinion. Consequently, the index system and early warning model constructed in this study have good feasibility and can provide references for related research on internet public opinion. INDEX TERMS Major emergencies, internet public opinion, Early warning index system, COVID-19, CRITIC, GA-BP neural network.

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Prediction of Entering Percentage into Expressway Service Areas Based on Wavelet Neural Networks and Genetic Algorithms

Cited by 19 publications

References 15 publications

OP Performance Prediction for Complex Mobile Multiuser Networks Based on Extreme Learning Machine

OP Performance Prediction for Complex Mobile Multiuser Networks Based on Extreme Learning Machine

Safety-oriented planning of expressway truck service areas based on driver demand

Research on the Early-Warning Model of Network Public Opinion of Major Emergencies

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