YaBeSH Engineering and Technology Library

    • Journals
    • PaperQuest
    • YSE Standards
    • YaBeSH
    • Login
    View Item 
    •   YE&T Library
    • ASCE
    • Journal of Transportation Engineering, Part A: Systems
    • View Item
    •   YE&T Library
    • ASCE
    • Journal of Transportation Engineering, Part A: Systems
    • View Item
    • All Fields
    • Source Title
    • Year
    • Publisher
    • Title
    • Subject
    • Author
    • DOI
    • ISBN
    Advanced Search
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Archive

    From Opacity to Clarity: Employing Explainable AI to Interpret CNN Predictions on Winter Road Conditions

    Source: Journal of Transportation Engineering, Part A: Systems:;2025:;Volume ( 151 ):;issue: 011::page 04025093-1
    Author:
    Wu, Mingjian
    ,
    Kwon, Tae J.
    DOI: 10.1061/JTEPBS.TEENG-8808
    Publisher: American Society of Civil Engineers
    Abstract: AbstractThe development of deep learning models for winter road surface conditions (RSC) classification has advanced in recent years. However, most of these models remain nontransparent, limiting confidence in their predictions. This study is a pioneering ...
    • Download: (2.294Mb)
    • Show Full MetaData Hide Full MetaData
    • Get RIS
    • Item Order
    • Go To Publisher
    • Statistics

      From Opacity to Clarity: Employing Explainable AI to Interpret CNN Predictions on Winter Road Conditions

    URI
    https://yetl.yabesh.ir/yetl1/handle/yetl/4313609
    Collections
    • Journal of Transportation Engineering, Part A: Systems

    Show full item record

    contributor authorWu, Mingjian
    contributor authorKwon, Tae J.
    date accessioned2026-08-20T20:52:41Z
    date available2026-08-20T20:52:41Z
    date copyright2025/09/15
    date issued2025
    identifier otherJTEPBS.TEENG-8808.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4313609
    description abstractAbstractThe development of deep learning models for winter road surface conditions (RSC) classification has advanced in recent years. However, most of these models remain nontransparent, limiting confidence in their predictions. This study is a pioneering ...
    publisherAmerican Society of Civil Engineers
    titleFrom Opacity to Clarity: Employing Explainable AI to Interpret CNN Predictions on Winter Road Conditions
    typeJournal Article
    journal volume151
    journal issue11
    journal titleJournal of Transportation Engineering, Part A: Systems
    identifier doi10.1061/JTEPBS.TEENG-8808
    journal fristpage04025093-1
    journal lastpage04025093-13
    page13
    treeJournal of Transportation Engineering, Part A: Systems:;2025:;Volume ( 151 ):;issue: 011
    contenttypeFulltext
    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian
     
    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian