Traffic Accidents Analytics in UK Urban Areas using k-means Clustering for Geospatial Mapping

Christopher Sinclair, Saptarshi Das
{"title":"Traffic Accidents Analytics in UK Urban Areas using k-means Clustering for Geospatial Mapping","authors":"Christopher Sinclair, Saptarshi Das","doi":"10.1109/SeFet48154.2021.9375817","DOIUrl":null,"url":null,"abstract":"The goal of this paper is to use the unsupervised machine learning method in road accident analytics, especially using k-means clustering to identify patterns and understand the relationships between variables recorded by the UK police department. These include features like number of casualties, number of vehicles, age of vehicle and age bracket of the driver. We aim to describe clusters of accidents based on similarity measures in the features and identify what separates each one.","PeriodicalId":232560,"journal":{"name":"2021 International Conference on Sustainable Energy and Future Electric Transportation (SEFET)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 International Conference on Sustainable Energy and Future Electric Transportation (SEFET)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SeFet48154.2021.9375817","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

Abstract

The goal of this paper is to use the unsupervised machine learning method in road accident analytics, especially using k-means clustering to identify patterns and understand the relationships between variables recorded by the UK police department. These include features like number of casualties, number of vehicles, age of vehicle and age bracket of the driver. We aim to describe clusters of accidents based on similarity measures in the features and identify what separates each one.
基于k-means聚类的英国城市交通事故分析
本文的目标是在道路事故分析中使用无监督机器学习方法,特别是使用k-means聚类来识别模式并理解英国警察部门记录的变量之间的关系。这些特征包括伤亡人数、车辆数量、车辆年龄和驾驶员的年龄段。我们的目标是基于特征中的相似性度量来描述事故集群,并确定每个集群之间的区别。
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 求助全文
来源期刊
自引率
0.00%
发文量
0
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信