Traffic Event Detection Using Online Social Networks

Alexandra S. Pereira, T. R. Silva, Fabrício A. Silva, A. Loureiro
{"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}
引用次数: 4

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.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于在线社交网络的流量事件检测
这项工作的重点是利用在线社交网络(OSNs)检测对大城市车辆交通有直接影响的事件,例如事故、道路建设翻新和交通堵塞。提出的模型旨在发现正在报告的问题,以及有关事件位置的信息。在对事件进行分类的任务中获得的结果是显著的,在确定通常交通事件的任务中达到高达94%的准确率和98%的一般命中率,此外在获得事件发生的城市点的参考方面也有希望的结果,召回率高达58%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Novel WSN Hardware for Long Range Low Power Monitoring Traffic Event Detection Using Online Social Networks TSCH and 6TiSCH for Contiki: Challenges, Design and Evaluation Measuring People-Flow through Doorways Using Easy-to-Install IR Array Sensors Towards more Realistic Models for Wireless Power Transfer Algorithm Design
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1