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Analysis of Factors Contributing to the Severity of Large Truck Crashes.
Li, Jinhong; Liu, Jinli; Liu, Pengfei; Qi, Yi.
Afiliação
  • Li J; School of Mathematics and Statistics, Qilu University of Technology (Shandong Academy of Sciences), University Road 3501, Jinan 250353, China.
  • Liu J; Department of Transportation Studies, Texas Southern University, 3100 Cleburne Street, Houston, TX 77004-9986, USA.
  • Liu P; Department of Civil and Environmental Engineering, the University of North Carolina at Charlotte, EPIC Building, Room 3366, 9201 University City Boulevard, Charlotte, NC 28223-0001, USA.
  • Qi Y; Department of Transportation Studies, Texas Southern University, 3100 Cleburne Street, Houston, TX 77004-9986, USA.
Entropy (Basel) ; 22(11)2020 Oct 22.
Artigo em Inglês | MEDLINE | ID: mdl-33286959
ABSTRACT
Crashes that involved large trucks often result in immense human, economic, and social losses. To prevent and mitigate severe large truck crashes, factors contributing to the severity of these crashes need to be identified before appropriate countermeasures can be explored. In this research, we applied three tree-based machine learning (ML) techniques, i.e., random forest (RF), gradient boost decision tree (GBDT), and adaptive boosting (AdaBoost), to analyze the factors contributing to the severity of large truck crashes. Besides, a mixed logit model was developed as a baseline model to compare with the factors identified by the ML models. The analysis was performed based on the crash data collected from the Texas Crash Records Information System (CRIS) from 2011 to 2015. The results of this research demonstrated that the GBDT model outperforms other ML methods in terms of its prediction accuracy and its capability in identifying more contributing factors that were also identified by the mixed logit model as significant factors. Besides, the GBDT method can effectively identify both categorical and numerical factors, and the directions and magnitudes of the impacts of the factors identified by the GBDT model are all reasonable and explainable. Among the identified factors, driving under the influence of drugs, alcohol, and fatigue are the most important factors contributing to the severity of large truck crashes. In addition, the exists of curbs and medians and lanes and shoulders with sufficient width can prevent severe large truck crashes.
Texto completo: Disponível Coleções: Bases de dados internacionais Base de dados: MEDLINE Idioma: Inglês Ano de publicação: 2020 Tipo de documento: Artigo País de afiliação: China

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Texto completo: Disponível Coleções: Bases de dados internacionais Base de dados: MEDLINE Idioma: Inglês Ano de publicação: 2020 Tipo de documento: Artigo País de afiliação: China