Predictive models for road traffic sign:Retroreflectivity status,retroreflectivity coefficient,and lifespan  

作　　者：Roxan Saleh Hasan Fleyeh 

机构地区：[1]School of Information and Engineering,Dalarna University,78170 Borlänge,Sweden [2]Swedish Transport Administration,Röda Vägen 1,78189 Borlänge,Sweden

出　　处：《International Journal of Transportation Science and Technology》2024年第4期276-291,共16页交通科学与技术(英文)

摘　　要：This study addresses the critical safety issue of declining retroreflectivity values of road traffic signs,which can lead to unsafe driving conditions,especially at night.The paper aims to predict the retroreflectivity coefficient values of these signs and to classify their status as acceptable or rejected(in need of replacement)using machine learning models.Moreover,logistic regression and survival analysis are used to predict the median lifespans of road traffic signs across various geographical locations,focusing on signs in Croatia and Sweden as case studies.The results indicate high accuracy in the predictive models,with classification accuracy at 94%and an R2 value of 94%for regression analysis.A significant finding is that a considerable number of signs maintain acceptable retroreflectivity levels within their warranty period,suggesting the feasibility of extending maintenance checks and warranty periods to 15 years which is longer than the current standard of 10 years.Additionally,the study reveals notable variations in the median lifespans of signs based on color and location.Blue signs in Croatia and Sweden exhibit the longest median lifespans(28 to 35 years),whereas white signs in Sweden and red signs in Croatia show the shortest(16 and 10 years,respectively).The high accuracy of logistic regression models(72-90%)for lifespan prediction confirms the effectiveness of this approach.These findings provide valuable insights for road authorities regarding the maintenance and management of road traffic signs,enhancing road safety standards.

关 键 词：Road sign Daylight chromaticity Retroreflectivity PREDICTION Machine learning algorithms 

分 类 号：TN9[电子电信—信息与通信工程]