Prediction of Fatalities at Northern Indian Railways’ Road–Rail Level Crossings Using Machine Learning Algorithms

IF 2.7 Q2 CONSTRUCTION & BUILDING TECHNOLOGY
Anil Kumar Chhotu, S. Suman
{"title":"Prediction of Fatalities at Northern Indian Railways’ Road–Rail Level Crossings Using Machine Learning Algorithms","authors":"Anil Kumar Chhotu, S. Suman","doi":"10.3390/infrastructures8060101","DOIUrl":null,"url":null,"abstract":"Highway railway level crossings, also widely recognized as HRLCs, present a significant threat to the safety of everyone who uses a roadway, including pedestrians who are attempting to cross an HRLC. More studies with new, proposed solutions are needed due to the global rise in HRLC accidents. Research is required to comprehend driver behaviours, user perceptions, and potential conflicts at level crossings, as well as for the accomplishment of preventative measures. The purpose of this study is to conduct an in-depth investigation of the HRLCs involved in accidents that are located in the northern zone of the Indian railway system. The accident information maintained by the distinct divisional and zonal offices in the northern railways of India is used for this study. The accident data revealed that at least 225 crossings experienced at least one incident between 2006 and 2021. In this study, the logistic regression and multilayer perception (MLP) methods are used to develop an accident prediction model, with the assistance of various factors from the incidents at HRLCs. Both the models were compared with each other, and it was discovered that MLP supplied the best results for accident predictions compared to the logistic regression method. According to the sensitivity analysis, the relative importance of train speed is the most important, and weekday traffic is the least important.","PeriodicalId":13601,"journal":{"name":"Infrastructures","volume":" ","pages":""},"PeriodicalIF":2.7000,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Infrastructures","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/infrastructures8060101","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CONSTRUCTION & BUILDING TECHNOLOGY","Score":null,"Total":0}
引用次数: 1

Abstract

Highway railway level crossings, also widely recognized as HRLCs, present a significant threat to the safety of everyone who uses a roadway, including pedestrians who are attempting to cross an HRLC. More studies with new, proposed solutions are needed due to the global rise in HRLC accidents. Research is required to comprehend driver behaviours, user perceptions, and potential conflicts at level crossings, as well as for the accomplishment of preventative measures. The purpose of this study is to conduct an in-depth investigation of the HRLCs involved in accidents that are located in the northern zone of the Indian railway system. The accident information maintained by the distinct divisional and zonal offices in the northern railways of India is used for this study. The accident data revealed that at least 225 crossings experienced at least one incident between 2006 and 2021. In this study, the logistic regression and multilayer perception (MLP) methods are used to develop an accident prediction model, with the assistance of various factors from the incidents at HRLCs. Both the models were compared with each other, and it was discovered that MLP supplied the best results for accident predictions compared to the logistic regression method. According to the sensitivity analysis, the relative importance of train speed is the most important, and weekday traffic is the least important.
使用机器学习算法预测印度北部铁路公铁平交道口的死亡人数
公路-铁路平交道口,也被广泛认为是HRLC,对所有使用道路的人的安全构成了重大威胁,包括试图穿过HRLC的行人。由于全球HRLC事故的增加,需要进行更多的研究,提出新的解决方案。需要进行研究,以了解驾驶员的行为、用户的感知和平交道口的潜在冲突,并完成预防措施。本研究的目的是对位于印度铁路系统北部地区的HRLC事故进行深入调查。本研究使用了印度北部铁路不同部门和地区办事处保存的事故信息。事故数据显示,2006年至2021年间,至少有225个过境点至少发生过一起事故。在本研究中,使用逻辑回归和多层感知(MLP)方法,在HRLC事件的各种因素的帮助下,开发了事故预测模型。将这两个模型相互比较,发现与逻辑回归方法相比,MLP为事故预测提供了最好的结果。根据敏感性分析,列车速度的相对重要性是最重要的,工作日交通量是最不重要的。
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
Infrastructures
Infrastructures Engineering-Building and Construction
CiteScore
5.20
自引率
7.70%
发文量
145
审稿时长
11 weeks
×
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