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Tytuł:
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A study of freeway crash risk prediction and interpretation based on risky driving behavior and traffic flow data.
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Autorzy:
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Guo M; Beijing Key Laboratory of Traffic Engineering and Beijing Engineering Research Center of Urban Transport Operation Guarantee, Beijing University of Technology, Beijing 100124, China.
Zhao X; Beijing Key Laboratory of Traffic Engineering and Beijing Engineering Research Center of Urban Transport Operation Guarantee, Beijing University of Technology, Beijing 100124, China.
Yao Y; Beijing Key Laboratory of Traffic Engineering and Beijing Engineering Research Center of Urban Transport Operation Guarantee, Beijing University of Technology, Beijing 100124, China. Electronic address: .
Yan P; Beijing Key Laboratory of Traffic Engineering and Beijing Engineering Research Center of Urban Transport Operation Guarantee, Beijing University of Technology, Beijing 100124, China.
Su Y; Traffic Management Solution Division AutoNavi Software Co., Beijing 100102, China.
Bi C; Traffic Management Solution Division AutoNavi Software Co., Beijing 100102, China.
Wu D; China Merchants New Intelligence Technology Co., Ltd., Beijing 100070, China.
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Źródło:
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Accident; analysis and prevention [Accid Anal Prev] 2021 Sep; Vol. 160, pp. 106328. Date of Electronic Publication: 2021 Aug 09.
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Typ publikacji:
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Journal Article
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Język:
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English
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Imprint Name(s):
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Publication: Oxford : Pergamon Press
Original Publication: [New York, Pergamon Press]
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MeSH Terms:
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Accidents, Traffic*
Automobile Driving*
Humans ; Logistic Models ; Risk-Taking ; Safety Management
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Contributed Indexing:
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Keywords: Logistic regression model; Risky driving behavior; Traffic crash risk prediction; Traffic flow
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Entry Date(s):
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Date Created: 20210813 Date Completed: 20210831 Latest Revision: 20210831
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Update Code:
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20240105
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DOI:
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10.1016/j.aap.2021.106328
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PMID:
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34385086
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The prediction of traffic crashes is an essential topic in traffic safety research. Most of the previous studies conducted experiments on real-time crash prediction of expressways or freeways, based on traffic flow data. However, the influence of risky driving behavior on traffic crash risk prediction has rarely been considered. Thus, a traffic crash risk prediction model based on risky driving behavior and traffic flow has been developed. The data employed in this research were captured using the in-vehicle AutoNavigator software. A random forest to select variables with strong impacts on crashes and the synthetic minority oversampling technique (SMOTE) to adjust the imbalanced dataset were included in the research. A logistic regression model was developed to predict the risk of traffic crash and to interpret its relationship with traffic flow and risky driving behavior characteristics. This model accurately predicted 84.48% of the crashes, while its false alarm rate remained as low as 9.75%, which indicated that this traffic crash risk prediction model had high accuracy. By analyzing the relationship between traffic flow, risky driving behavior, and crashes through partial dependency plots (PDPs), the impact of traffic flow and risky driving behavior variables on certain traffic crashes in the prediction model were determined. Through this study, the data of traffic flow and risky driving behavior could be used to assess the traffic crash risk on freeways and lay a foundation for traffic safety management.
(Copyright © 2021 Elsevier Ltd. All rights reserved.)
