基于神经网络模型的智慧城市交通评价系统

IF 0.5 Q4 ENVIRONMENTAL STUDIES
Mingyue Wang
{"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}
引用次数: 0

摘要

基于绿色交通与重组的基本内涵和绿色交通的“五位一体”理论,构建了城市绿色交通的评价指标体系,提出了基于BP神经网络的评价模型,并对其进行了验证。本文验证了该方法的效率和合理性,确定了网络层数、传递函数、训练函数、隐藏层神经元的数量,并提供了可行的评估方案,利用MATLAB神经网络工具箱(NNT)设计计算网络,并利用样本训练进行仿真测试。从结果可以看出,城市生态交通BP神经网络评价模型的准确性较高。训练精度可达3.4*10−3量级,输出精度可达10−4量级,模型误差在预定范围内。提出了发展城市生态交通的战略措施。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
1.50
自引率
0.00%
发文量
58
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.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信