Alexandra S. Pereira, T. R. Silva, Fabrício A. Silva, A. Loureiro
{"title":"基于在线社交网络的流量事件检测","authors":"Alexandra S. Pereira, T. R. Silva, Fabrício A. Silva, A. Loureiro","doi":"10.1109/DCOSS.2017.36","DOIUrl":null,"url":null,"abstract":"The focus of this work is on the detection of incidents that have a direct impact on the traffic of vehicles in large cities, such as, accidents, road constructions-renovations and traffic jams using Online Social Networks(OSNs). The proposed model aims to find problems being reported, as well as information on the location of the event. The results obtained were significant in the task of categorizing the incident, reaching up to 94% accuracy and 98% of general hits in the task of determining usual traffic incidents, besides promising results in obtaining references to the points in the city where the incidents take place, with up to 58% recall.","PeriodicalId":399222,"journal":{"name":"2017 13th International Conference on Distributed Computing in Sensor Systems (DCOSS)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Traffic Event Detection Using Online Social Networks\",\"authors\":\"Alexandra S. Pereira, T. R. Silva, Fabrício A. Silva, A. Loureiro\",\"doi\":\"10.1109/DCOSS.2017.36\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The focus of this work is on the detection of incidents that have a direct impact on the traffic of vehicles in large cities, such as, accidents, road constructions-renovations and traffic jams using Online Social Networks(OSNs). The proposed model aims to find problems being reported, as well as information on the location of the event. The results obtained were significant in the task of categorizing the incident, reaching up to 94% accuracy and 98% of general hits in the task of determining usual traffic incidents, besides promising results in obtaining references to the points in the city where the incidents take place, with up to 58% recall.\",\"PeriodicalId\":399222,\"journal\":{\"name\":\"2017 13th International Conference on Distributed Computing in Sensor Systems (DCOSS)\",\"volume\":\"54 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-06-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 13th International Conference on Distributed Computing in Sensor Systems (DCOSS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/DCOSS.2017.36\",\"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 13th International Conference on Distributed Computing in Sensor Systems (DCOSS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DCOSS.2017.36","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Traffic Event Detection Using Online Social Networks
The focus of this work is on the detection of incidents that have a direct impact on the traffic of vehicles in large cities, such as, accidents, road constructions-renovations and traffic jams using Online Social Networks(OSNs). The proposed model aims to find problems being reported, as well as information on the location of the event. The results obtained were significant in the task of categorizing the incident, reaching up to 94% accuracy and 98% of general hits in the task of determining usual traffic incidents, besides promising results in obtaining references to the points in the city where the incidents take place, with up to 58% recall.