{"title":"基于时空网络的交通事故聚类检测","authors":"Keisuke Ando , Yusuke Kuniyoshi , Natsuki Onogi , Takeshi Uchitane , Naoto Mukai , Kazunori Iwata , Nobuhiro Ito , Yong Jiang","doi":"10.1016/j.procs.2024.09.416","DOIUrl":null,"url":null,"abstract":"<div><div>Traffic safety measures are essential for addressing traffic accident clusters, which denote specific locations and times at which traffic accidents are prone. This study introduces a novel approach for identifying these clusters through hypothesis testing of the spatiotemporal network of previous traffic accidents. The experimental results across various regions in Aichi Prefecture are more accurate than those of a previous method for detecting these clusters.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"246 ","pages":"Pages 371-380"},"PeriodicalIF":0.0000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Cluster Detection for Traffic Accidents on Spatiotemporal Networks\",\"authors\":\"Keisuke Ando , Yusuke Kuniyoshi , Natsuki Onogi , Takeshi Uchitane , Naoto Mukai , Kazunori Iwata , Nobuhiro Ito , Yong Jiang\",\"doi\":\"10.1016/j.procs.2024.09.416\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Traffic safety measures are essential for addressing traffic accident clusters, which denote specific locations and times at which traffic accidents are prone. This study introduces a novel approach for identifying these clusters through hypothesis testing of the spatiotemporal network of previous traffic accidents. The experimental results across various regions in Aichi Prefecture are more accurate than those of a previous method for detecting these clusters.</div></div>\",\"PeriodicalId\":20465,\"journal\":{\"name\":\"Procedia Computer Science\",\"volume\":\"246 \",\"pages\":\"Pages 371-380\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Procedia Computer Science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1877050924024566\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Procedia Computer Science","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1877050924024566","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Cluster Detection for Traffic Accidents on Spatiotemporal Networks
Traffic safety measures are essential for addressing traffic accident clusters, which denote specific locations and times at which traffic accidents are prone. This study introduces a novel approach for identifying these clusters through hypothesis testing of the spatiotemporal network of previous traffic accidents. The experimental results across various regions in Aichi Prefecture are more accurate than those of a previous method for detecting these clusters.
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