Risk prediction for cut-ins using multi-driver simulation data and machine learning algorithms: A comparison among decision tree, GBDT and LSTM

Risk prediction for cut-ins using multi-driver simulation data and machine learning algorithms: A comparison among decision tree, GBDT and LSTM

The cut-ins (one kind of lane-changing behaviors) have result in severe safety issues, especially at the entrances and exits of urban expressways. Risk prediction and characteristics analysis of cut-ins are part of the essential research for advanced in-vehicle technologies which can reduce crash oc...

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Bibliographic Details
Main Authors: Tianyang Luo, Junhua Wang, Ting Fu, Qiangqiang Shangguan, Shou'en Fang
Format: Article
Language:English
Published: KeAi Communications Co., Ltd. 2023-09-01
Series:International Journal of Transportation Science and Technology
Subjects:
Multi-driver simulation platform
Cut-in events
Risk prediction
Urban expressway
Machine learning approach
Online Access:http://www.sciencedirect.com/science/article/pii/S2046043022001010

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http://www.sciencedirect.com/science/article/pii/S2046043022001010

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