{"title":"利用智能手机GPS提取道路","authors":"Z. Niu, Songnian Li, Neda Pousaeid","doi":"10.1145/1999320.1999342","DOIUrl":null,"url":null,"abstract":"GPS data crowd-sourced through smart phones is an emerging source of inexpensive data that can be used to provide real-time traffic information, identify traffic patterns, and predict traffic congestions. The same type of data can be very useful for cost-effective, fast updating of road network databases due to its rich spatial and temporal coverage and high data volume. This paper presents the results of a study that extracts road geometry data using GPS data received from smart phones. The focus is on the method for road centerlines and the study presents some promising results. It is expected the method can supplement, if not replace, the current practices of acquiring road network data using traditional expensive and time consuming survey or remote sensing approaches.","PeriodicalId":400763,"journal":{"name":"International Conference and Exhibition on Computing for Geospatial Research & Application","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"16","resultStr":"{\"title\":\"Road extraction using smart phones GPS\",\"authors\":\"Z. Niu, Songnian Li, Neda Pousaeid\",\"doi\":\"10.1145/1999320.1999342\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"GPS data crowd-sourced through smart phones is an emerging source of inexpensive data that can be used to provide real-time traffic information, identify traffic patterns, and predict traffic congestions. The same type of data can be very useful for cost-effective, fast updating of road network databases due to its rich spatial and temporal coverage and high data volume. This paper presents the results of a study that extracts road geometry data using GPS data received from smart phones. The focus is on the method for road centerlines and the study presents some promising results. It is expected the method can supplement, if not replace, the current practices of acquiring road network data using traditional expensive and time consuming survey or remote sensing approaches.\",\"PeriodicalId\":400763,\"journal\":{\"name\":\"International Conference and Exhibition on Computing for Geospatial Research & Application\",\"volume\":\"21 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-05-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"16\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Conference and Exhibition on Computing for Geospatial Research & Application\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/1999320.1999342\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Conference and Exhibition on Computing for Geospatial Research & Application","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1999320.1999342","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
GPS data crowd-sourced through smart phones is an emerging source of inexpensive data that can be used to provide real-time traffic information, identify traffic patterns, and predict traffic congestions. The same type of data can be very useful for cost-effective, fast updating of road network databases due to its rich spatial and temporal coverage and high data volume. This paper presents the results of a study that extracts road geometry data using GPS data received from smart phones. The focus is on the method for road centerlines and the study presents some promising results. It is expected the method can supplement, if not replace, the current practices of acquiring road network data using traditional expensive and time consuming survey or remote sensing approaches.