M. V. D. Kerkhof, I. Kostitsyna, M. V. Kreveld, M. Löffler, Tim Ophelders
{"title":"路线保持路网泛化","authors":"M. V. D. Kerkhof, I. Kostitsyna, M. V. Kreveld, M. Löffler, Tim Ophelders","doi":"10.1145/3397536.3422234","DOIUrl":null,"url":null,"abstract":"We investigate a data-driven approach for road network generalization, where the input is a road network and a collection of routes or trajectories on these roads. The aim is to select a subset of the road network in which many routes of the collection are fully preserved. We formulate the problem and present several heuristic versions of it, as the general problem is NP-hard. We show the outcome of the versions on a data set for comparison purposes.","PeriodicalId":233918,"journal":{"name":"Proceedings of the 28th International Conference on Advances in Geographic Information Systems","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Route-preserving Road Network Generalization\",\"authors\":\"M. V. D. Kerkhof, I. Kostitsyna, M. V. Kreveld, M. Löffler, Tim Ophelders\",\"doi\":\"10.1145/3397536.3422234\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We investigate a data-driven approach for road network generalization, where the input is a road network and a collection of routes or trajectories on these roads. The aim is to select a subset of the road network in which many routes of the collection are fully preserved. We formulate the problem and present several heuristic versions of it, as the general problem is NP-hard. We show the outcome of the versions on a data set for comparison purposes.\",\"PeriodicalId\":233918,\"journal\":{\"name\":\"Proceedings of the 28th International Conference on Advances in Geographic Information Systems\",\"volume\":\"9 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-11-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 28th International Conference on Advances in Geographic Information Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3397536.3422234\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 28th International Conference on Advances in Geographic Information Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3397536.3422234","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
We investigate a data-driven approach for road network generalization, where the input is a road network and a collection of routes or trajectories on these roads. The aim is to select a subset of the road network in which many routes of the collection are fully preserved. We formulate the problem and present several heuristic versions of it, as the general problem is NP-hard. We show the outcome of the versions on a data set for comparison purposes.