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Assessing Pavement Conditions Impact on Traffic Crashes: An Interpretable Machine-Learning Approach to Identify Crash Risk Factors and Performance Thresholds

dc.contributor.authorYao, Linyi
dc.contributor.authorLeng, Zhen
dc.contributor.authorJiang, Jiwang
dc.contributor.authorZheng, Junqiu
dc.contributor.authorNi, Fujian
dc.date.accessioned2026-08-20T12:01:45Z
dc.date.available2026-08-20T12:01:45Z
dc.date.copyright2025/10/06
dc.date.issued2025
dc.identifier.otherJPEODX.PVENG-1824.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4312996
dc.description.abstractAbstractPavement surface condition may have a significant impact on traffic accidents, but its influencing mechanisms remain unclear, and safety-oriented maintenance thresholds are still lacking. To addresses these gaps, this study develops a highway ...
dc.publisherAmerican Society of Civil Engineers
dc.titleAssessing Pavement Conditions Impact on Traffic Crashes: An Interpretable Machine-Learning Approach to Identify Crash Risk Factors and Performance Thresholds
dc.typeJournal Article
dc.journal.volume151
dc.journal.issue4
dc.journal.titleJournal of Transportation Engineering, Part B: Pavements
dc.identifier.doi10.1061/JPEODX.PVENG-1824
dc.journal.fristpage04025049-1
dc.journal.lastpage04025049-13
dc.page13
dc.treeJournal of Transportation Engineering, Part B: Pavements:;2025:;Volume ( 151 ):;issue: 004
dc.contenttypeFulltext


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