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

dc.contributor.author	Wu, Mingjian
dc.contributor.author	Kwon, Tae J.
dc.date.accessioned	2026-08-20T20:52:41Z
dc.date.available	2026-08-20T20:52:41Z
dc.date.copyright	2025/09/15
dc.date.issued	2025
dc.identifier.other	JTEPBS.TEENG-8808.pdf
dc.identifier.uri	http://yetl.yabesh.ir/yetl1/handle/yetl/4313609
dc.description.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 ...
dc.publisher	American Society of Civil Engineers
dc.title	From Opacity to Clarity: Employing Explainable AI to Interpret CNN Predictions on Winter Road Conditions
dc.type	Journal Article
dc.journal.volume	151
dc.journal.issue	11
dc.journal.title	Journal of Transportation Engineering, Part A: Systems
dc.identifier.doi	10.1061/JTEPBS.TEENG-8808
dc.journal.fristpage	04025093-1
dc.journal.lastpage	04025093-13
dc.page	13
dc.tree	Journal of Transportation Engineering, Part A: Systems:;2025:;Volume ( 151 ):;issue: 011
dc.contenttype	Fulltext