{"title":"基于灰色神经网络的短期交通预测模型","authors":"Qiongqin Jiang, Zhigang Liu, Youfu Du","doi":"10.1109/WGEC.2009.159","DOIUrl":null,"url":null,"abstract":"This paper expounds three kinds of grey neural network combined model for short-term prediction of urban traffic speed, and confirms their validity and feasibility by conducting experiment in Beijing road of Jingzhou. Three kinds of networks are parallel grey neural network, series grey neural network, and inlaid grey neural network. The experiment proves that the three kinds of modes are feasible and effective in comparison with single model GM(1,1) and neural network. And actual traffic speed varies smoothly or will not influence significantly the accuracy for prediction.","PeriodicalId":277950,"journal":{"name":"2009 Third International Conference on Genetic and Evolutionary Computing","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Short-term Traffic Prediction Model Based on Grey Neural Network\",\"authors\":\"Qiongqin Jiang, Zhigang Liu, Youfu Du\",\"doi\":\"10.1109/WGEC.2009.159\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper expounds three kinds of grey neural network combined model for short-term prediction of urban traffic speed, and confirms their validity and feasibility by conducting experiment in Beijing road of Jingzhou. Three kinds of networks are parallel grey neural network, series grey neural network, and inlaid grey neural network. The experiment proves that the three kinds of modes are feasible and effective in comparison with single model GM(1,1) and neural network. And actual traffic speed varies smoothly or will not influence significantly the accuracy for prediction.\",\"PeriodicalId\":277950,\"journal\":{\"name\":\"2009 Third International Conference on Genetic and Evolutionary Computing\",\"volume\":\"7 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-10-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 Third International Conference on Genetic and Evolutionary Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/WGEC.2009.159\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 Third International Conference on Genetic and Evolutionary Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WGEC.2009.159","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Short-term Traffic Prediction Model Based on Grey Neural Network
This paper expounds three kinds of grey neural network combined model for short-term prediction of urban traffic speed, and confirms their validity and feasibility by conducting experiment in Beijing road of Jingzhou. Three kinds of networks are parallel grey neural network, series grey neural network, and inlaid grey neural network. The experiment proves that the three kinds of modes are feasible and effective in comparison with single model GM(1,1) and neural network. And actual traffic speed varies smoothly or will not influence significantly the accuracy for prediction.