{"title":"基于历史数据和共享位置的车辆行驶时间预测算法","authors":"Peng Chen, Zhao Lu, Junzhong Gu","doi":"10.1109/NCM.2009.138","DOIUrl":null,"url":null,"abstract":"In recent years, the travel time predictions have become a popular research topic. In this paper, we present a new algorithm of the travel time predictions based on the idea of using the shared traveler’s positions to collect traffic conditions. Several experiments show that our algorithm has a broader applied area than existing algorithms and can provide real-time and the accurate predictions for the travelers. And when there are more travelers and more positions shared among them, the more accurate predictions of our algorithm will be.","PeriodicalId":119669,"journal":{"name":"2009 Fifth International Joint Conference on INC, IMS and IDC","volume":"25 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":"{\"title\":\"Vehicle Travel Time Prediction Algorithm Based on Historical Data and Shared Location\",\"authors\":\"Peng Chen, Zhao Lu, Junzhong Gu\",\"doi\":\"10.1109/NCM.2009.138\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In recent years, the travel time predictions have become a popular research topic. In this paper, we present a new algorithm of the travel time predictions based on the idea of using the shared traveler’s positions to collect traffic conditions. Several experiments show that our algorithm has a broader applied area than existing algorithms and can provide real-time and the accurate predictions for the travelers. And when there are more travelers and more positions shared among them, the more accurate predictions of our algorithm will be.\",\"PeriodicalId\":119669,\"journal\":{\"name\":\"2009 Fifth International Joint Conference on INC, IMS and IDC\",\"volume\":\"25 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-08-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"13\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 Fifth International Joint Conference on INC, IMS and IDC\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NCM.2009.138\",\"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 Fifth International Joint Conference on INC, IMS and IDC","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NCM.2009.138","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Vehicle Travel Time Prediction Algorithm Based on Historical Data and Shared Location
In recent years, the travel time predictions have become a popular research topic. In this paper, we present a new algorithm of the travel time predictions based on the idea of using the shared traveler’s positions to collect traffic conditions. Several experiments show that our algorithm has a broader applied area than existing algorithms and can provide real-time and the accurate predictions for the travelers. And when there are more travelers and more positions shared among them, the more accurate predictions of our algorithm will be.