A genetic programming approach for real-time crash prediction to solve trade-off between interpretability and accuracy

IF 2.4 3区 工程技术 Q3 TRANSPORTATION Journal of Transportation Safety & Security Pub Date : 2022-05-31 DOI:10.1080/19439962.2022.2076756
Xiaochi Ma, Jian Lu, Xian Liu, Weibin Qu
{"title":"A genetic programming approach for real-time crash prediction to solve trade-off between interpretability and accuracy","authors":"Xiaochi Ma, Jian Lu, Xian Liu, Weibin Qu","doi":"10.1080/19439962.2022.2076756","DOIUrl":null,"url":null,"abstract":"Abstract Real-time crash risk prediction is a hot topic of emerging technology. Due to the lack of basic risk formation theory, previous studies focussed on the application of complex models to improve the accuracy of prediction, ignoring the interpretation of variables, while the traditional statistical analysis method can interpret variables, but the prediction accuracy is poor, which falls into a dilemma of trade-off. In this study, based on the traffic flow information of elevated expressway, an improved genetic programming (GP) approach with elite gene bank is applied to obtain an explicit traffic flow crash risk function to solve the above trade-off problem. Logistic regression and backward-propagation neural network combined with partial dependency plot were used as baseline methods to examine the interpretability and accuracy of GP. It is found that GP prediction model has been proved to be able to select important variables and solve the trade-off dilemma, which has good interpretability and accuracy. The results show that crash risk in the traffic flow mainly comes from the traffic volume, speed of the upstream section, and the speed of the current section. Furthermore, the error of GP comes from the unobserved heterogeneity and crash mechanism theory is proposed.","PeriodicalId":46672,"journal":{"name":"Journal of Transportation Safety & Security","volume":"65 1","pages":"421 - 443"},"PeriodicalIF":2.4000,"publicationDate":"2022-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Transportation Safety & Security","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1080/19439962.2022.2076756","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"TRANSPORTATION","Score":null,"Total":0}
引用次数: 5

Abstract

Abstract Real-time crash risk prediction is a hot topic of emerging technology. Due to the lack of basic risk formation theory, previous studies focussed on the application of complex models to improve the accuracy of prediction, ignoring the interpretation of variables, while the traditional statistical analysis method can interpret variables, but the prediction accuracy is poor, which falls into a dilemma of trade-off. In this study, based on the traffic flow information of elevated expressway, an improved genetic programming (GP) approach with elite gene bank is applied to obtain an explicit traffic flow crash risk function to solve the above trade-off problem. Logistic regression and backward-propagation neural network combined with partial dependency plot were used as baseline methods to examine the interpretability and accuracy of GP. It is found that GP prediction model has been proved to be able to select important variables and solve the trade-off dilemma, which has good interpretability and accuracy. The results show that crash risk in the traffic flow mainly comes from the traffic volume, speed of the upstream section, and the speed of the current section. Furthermore, the error of GP comes from the unobserved heterogeneity and crash mechanism theory is proposed.
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
一种解决可解释性和准确性之间权衡的实时碰撞预测遗传规划方法
实时碰撞风险预测是新兴技术的一个热点。由于缺乏基本的风险形成理论,以往的研究多侧重于应用复杂模型来提高预测精度,忽略了对变量的解释,而传统的统计分析方法虽然可以解释变量,但预测精度较差,陷入取舍的困境。本研究基于高架高速公路交通流信息,采用改进的遗传规划(GP)方法,结合精英基因库,得到明确的交通流碰撞风险函数,以解决上述权衡问题。采用Logistic回归和后向传播神经网络结合部分依赖图作为基线方法来检验GP的可解释性和准确性。结果表明,GP预测模型能够选择重要变量并解决权衡困境,具有良好的可解释性和准确性。结果表明,交通流中的碰撞风险主要来自于车流量、上游路段的车速和当前路段的车速。此外,还提出了GP的误差来自于未观测到的异质性和崩溃机制理论。
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
CiteScore
6.00
自引率
15.40%
发文量
38
期刊最新文献
Examining the crash risk factors associated with cycling by considering spatial and temporal disaggregation of exposure: Findings from four Dutch cities Traffic safety performance evaluation in a connected vehicle environment with queue warning and speed harmonization applications Enhancing bicyclist survival time in fatal crashes: Investigating the impact of faster crash notification time through explainable machine learning Factors affecting pedestrian injury severity in pedestrian-vehicle crashes: Insights from a data mining and mixed logit model approach Prediction of high-risk bus drivers characterized by aggressive driving behavior
×
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