Chapter
Jul 2, 2019
A Machine Learning Approach for Highway Intersection Risk Caused by Harmful Lane-Changing Behaviors
Authors: Yanping Hao [email protected], Liangjie Xu [email protected], Bozhao Qi [email protected], Teng Wang [email protected], and Wei Zhao [email protected]Author Affiliations
Publication: CICTP 2019
Abstract
Highway intersection-related crashes are suspected to be associated with harmful lane-changing behaviors. To better understand the relationship between them, this study applied an innovative machine learning approach to identify crash risk factors and find solutions to reduce the intersection-related crash frequency and severity caused by harmful lane-changing behaviors. First, a vehicles approach time (VAT) model was developed to define and classify different types of harmful lane-changing behaviors. Second, the real world driving video data was collected and preprocessed to identify the potential crash risk factors of harmful lane-changing behaviors. Finally, an advanced machine learning algorithm, Lasso-LARS, was applied to analyze the relation between intersection-related crash risk factors and lane-changing behaviors. There were no significant differences in the VAT values between the VAT model and the Lasso-LARS regression model. The result shows that both the two models are suitable for the analysis of risk factors of harmful lane-changing behaviors.
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© 2019 American Society of Civil Engineers.
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Published online: Jul 2, 2019
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Dept. of Traffic Engineering, School of Transportation, Wuhan Univ. of Technology, Wuhan 430063, China; State of Key Laboratory of Vehicle of VNH and Safety Technology, Chongqing, China. E-mail: [email protected]
Dept. of Traffic Engineering, School of Transportation, Wuhan Univ. of Technology, Wuhan 430063, China. E-mail: [email protected]
Dept. of Electrical and Computer Engineering, Univ. of Wisconsin–Madison, Madison, WI 53705. E-mail: [email protected]
Texas A&M Transportation Institute, San Antonio, TX. E-mail: [email protected]
Dept. of Traffic Engineering, School of Transportation, Wuhan Univ. of Technology, Wuhan 430063, China. E-mail: [email protected]
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