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Traffic Conflict Identification and Risk Assessment at Ramp Merging Areas Based on Improved XGBoost and SHAP

dc.contributor.authorTian, Yuan
dc.contributor.authorZhang, Zihao
dc.contributor.authorLi, Zhouyuan
dc.contributor.authorFu, Xueqing
dc.contributor.authorWang, Guangmao
dc.contributor.authorMa, Yuan
dc.contributor.authorWu, Jianqing
dc.date.accessioned2026-08-20T20:53:13Z
dc.date.available2026-08-20T20:53:13Z
dc.date.copyright2025/09/09
dc.date.issued2025
dc.identifier.otherJTEPBS.TEENG-8918.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4313619
dc.description.abstractAbstractRamp merging areas are high-risk zones for traffic accidents due to the high traffic density and frequent changes in vehicle speed. This study proposes a comprehensive approach that integrates machine learning with feature interpretation to ...Practical ApplicationsRamp merging areas, where vehicles enter or exit highways, are known to be high-risk zones for accidents due to heavy traffic and frequent speed fluctuations. This study offers a new method for assessing the risk of traffic conflicts ...
dc.publisherAmerican Society of Civil Engineers
dc.titleTraffic Conflict Identification and Risk Assessment at Ramp Merging Areas Based on Improved XGBoost and SHAP
dc.typeJournal Article
dc.journal.volume151
dc.journal.issue11
dc.journal.titleJournal of Transportation Engineering, Part A: Systems
dc.identifier.doi10.1061/JTEPBS.TEENG-8918
dc.journal.fristpage04025091-1
dc.journal.lastpage04025091-11
dc.page11
dc.treeJournal of Transportation Engineering, Part A: Systems:;2025:;Volume ( 151 ):;issue: 011
dc.contenttypeFulltext


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