{"title":"基于神经网络模型的智慧城市交通评价系统","authors":"Mingyue Wang","doi":"10.1504/ijgei.2023.133807","DOIUrl":null,"url":null,"abstract":"Based on the basic connotation of green transportation and reorganisation and the five-in-one theory of green transportation, the article constructs an evaluation index system for urban green transportation, proposes an evaluation model based on BP neural network, and tests it. The article verifies the efficiency and rationality of this method, determines the number of network layers, transfer function, training function, hidden layer neurons, and provides a feasible evaluation program, uses MATLAB Neural Network Toolbox (NNT) to design the calculation network, and uses sample training for simulation testing. From the results, it can be seen that the accuracy of the urban ecological transportation BP neural network evaluation model is relatively high. The training accuracy can reach 3.4*10−3 magnitude, the output accuracy can reach 10−4 magnitude, and the error of the model is within a predetermined range. The strategic measures for the development of urban ecological transportation are proposed.","PeriodicalId":51891,"journal":{"name":"INTERNATIONAL JOURNAL OF GLOBAL ENERGY ISSUES","volume":null,"pages":null},"PeriodicalIF":0.5000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Smart city traffic evaluation system based on neural network model\",\"authors\":\"Mingyue Wang\",\"doi\":\"10.1504/ijgei.2023.133807\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Based on the basic connotation of green transportation and reorganisation and the five-in-one theory of green transportation, the article constructs an evaluation index system for urban green transportation, proposes an evaluation model based on BP neural network, and tests it. The article verifies the efficiency and rationality of this method, determines the number of network layers, transfer function, training function, hidden layer neurons, and provides a feasible evaluation program, uses MATLAB Neural Network Toolbox (NNT) to design the calculation network, and uses sample training for simulation testing. From the results, it can be seen that the accuracy of the urban ecological transportation BP neural network evaluation model is relatively high. The training accuracy can reach 3.4*10−3 magnitude, the output accuracy can reach 10−4 magnitude, and the error of the model is within a predetermined range. The strategic measures for the development of urban ecological transportation are proposed.\",\"PeriodicalId\":51891,\"journal\":{\"name\":\"INTERNATIONAL JOURNAL OF GLOBAL ENERGY ISSUES\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.5000,\"publicationDate\":\"2023-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"INTERNATIONAL JOURNAL OF GLOBAL ENERGY ISSUES\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1504/ijgei.2023.133807\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ENVIRONMENTAL STUDIES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"INTERNATIONAL JOURNAL OF GLOBAL ENERGY ISSUES","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1504/ijgei.2023.133807","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENVIRONMENTAL STUDIES","Score":null,"Total":0}
Smart city traffic evaluation system based on neural network model
Based on the basic connotation of green transportation and reorganisation and the five-in-one theory of green transportation, the article constructs an evaluation index system for urban green transportation, proposes an evaluation model based on BP neural network, and tests it. The article verifies the efficiency and rationality of this method, determines the number of network layers, transfer function, training function, hidden layer neurons, and provides a feasible evaluation program, uses MATLAB Neural Network Toolbox (NNT) to design the calculation network, and uses sample training for simulation testing. From the results, it can be seen that the accuracy of the urban ecological transportation BP neural network evaluation model is relatively high. The training accuracy can reach 3.4*10−3 magnitude, the output accuracy can reach 10−4 magnitude, and the error of the model is within a predetermined range. The strategic measures for the development of urban ecological transportation are proposed.
期刊介绍:
IJGEI is a refereed, international journal providing an international forum and authoritative source of information, analyses and discussions on renewable and non-renewable energy resources, energy-economic systems, energy and environment, international energy policy issues, technological innovation and new energy sources. Since the 1970s, attention has been focused on energy resources in the search for sustainable and environmentally non-destructive economic development. The confrontation of ecological limits to growth is not only a technological challenge. Economic, social and natural sciences must be brought together in new perspectives, responding to the concerns expressed worldwide for ecological, social, economic and political dimensions of sustainability.