{"title":"基于DGM(1,1)和GRNN的交通流联合预测模型","authors":"Zhiheng Yu, Kecheng Liu, Chengli Zhao, Y. Liu","doi":"10.1109/CIIS.2017.17","DOIUrl":null,"url":null,"abstract":"In order to improve the prediction accuracy of traffic flow, this paper proposes a combined forecasting models with residual correction based on DGM(1,1). After introducing the modeling steps of DGM(1,1), DGM-GRNN combination model is established. In the model, the GRNN is used to predict the residual of DGM(1,1) model. Finally, we sum up the predict result of DGM(1,1) model and the residual correction model, and get the final prediction result of the combined forecasting model. We predicted the traffic flow of shangsan expressway by the model in this paper and then compared the results with the experimental results of DGM(1,1). The validity and feasibility of the proposed method are verified.","PeriodicalId":254342,"journal":{"name":"2017 International Conference on Computing Intelligence and Information System (CIIS)","volume":"332 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Combined Forecasting Model of Traffic Flow Based on DGM(1,1) and GRNN\",\"authors\":\"Zhiheng Yu, Kecheng Liu, Chengli Zhao, Y. Liu\",\"doi\":\"10.1109/CIIS.2017.17\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In order to improve the prediction accuracy of traffic flow, this paper proposes a combined forecasting models with residual correction based on DGM(1,1). After introducing the modeling steps of DGM(1,1), DGM-GRNN combination model is established. In the model, the GRNN is used to predict the residual of DGM(1,1) model. Finally, we sum up the predict result of DGM(1,1) model and the residual correction model, and get the final prediction result of the combined forecasting model. We predicted the traffic flow of shangsan expressway by the model in this paper and then compared the results with the experimental results of DGM(1,1). The validity and feasibility of the proposed method are verified.\",\"PeriodicalId\":254342,\"journal\":{\"name\":\"2017 International Conference on Computing Intelligence and Information System (CIIS)\",\"volume\":\"332 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 International Conference on Computing Intelligence and Information System (CIIS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CIIS.2017.17\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 International Conference on Computing Intelligence and Information System (CIIS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CIIS.2017.17","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Combined Forecasting Model of Traffic Flow Based on DGM(1,1) and GRNN
In order to improve the prediction accuracy of traffic flow, this paper proposes a combined forecasting models with residual correction based on DGM(1,1). After introducing the modeling steps of DGM(1,1), DGM-GRNN combination model is established. In the model, the GRNN is used to predict the residual of DGM(1,1) model. Finally, we sum up the predict result of DGM(1,1) model and the residual correction model, and get the final prediction result of the combined forecasting model. We predicted the traffic flow of shangsan expressway by the model in this paper and then compared the results with the experimental results of DGM(1,1). The validity and feasibility of the proposed method are verified.