The application of XGBoost and SHAP to examining the factors in freight truck-related crashes: An exploratory analysis

•Spatial distribution of freight truck related crashes differs by injury severity.•In contrast with ZIP model, XGBoost model is used to test nonlinear relationships.•Demographics, land uses and road network matter in occurrence of those crashes.•SHAP method is employed to display details of nonlinea...

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Vydáno v:Accident analysis and prevention Ročník 158; s. 106153
Hlavní autoři: Yang, Chao, Chen, Mingyang, Yuan, Quan
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.08.2021
Témata:
Built environment
Freight truck-related crashes
Injury severity
Shapley Additive exPlanations
XGBoost
Zero Inflated Poisson regression
Built environment
Freight truck-related crashes
XGBoost
Injury severity
Shapley Additive exPlanations
Zero Inflated Poisson regression
ISSN:0001-4575, 1879-2057, 1879-2057
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Tagy: Přidat tag
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Abstract •Spatial distribution of freight truck related crashes differs by injury severity.•In contrast with ZIP model, XGBoost model is used to test nonlinear relationships.•Demographics, land uses and road network matter in occurrence of those crashes.•SHAP method is employed to display details of nonlinearity in XGBoost model.•Impacts of built environment vary across contexts due to nonlinear relationships. Due to the burgeoning demand for freight movement, freight related road safety threats have been growing substantially. In spite of some research on the factors influencing freight truck-related crashes in major cities, the literature offers limited evidence about the effects of the built environment on the occurrence of those crashes by injury severity. This article uses data from the Los Angeles region in 2010–2019 to explore the relationships between the built environment factors and the spatial distribution of freight truck-related crashes using XGBoost and SHAP methods. Results from the XGBoost model show that variables related to the built environment, in particular demographics, land uses and road network, are highly correlated to freight truck related crashes of all three injury types. The SHAP value plots further indicate the important nonlinear relationships between independent variables and dependent variables. This study also emphasizes the differences in modeling mechanisms between the XGBoost model and traditional statistical models. The findings will help transport planners develop operational measures for resolving the emerging freight truck related traffic safety problems in local communities.
AbstractList •Spatial distribution of freight truck related crashes differs by injury severity.•In contrast with ZIP model, XGBoost model is used to test nonlinear relationships.•Demographics, land uses and road network matter in occurrence of those crashes.•SHAP method is employed to display details of nonlinearity in XGBoost model.•Impacts of built environment vary across contexts due to nonlinear relationships. Due to the burgeoning demand for freight movement, freight related road safety threats have been growing substantially. In spite of some research on the factors influencing freight truck-related crashes in major cities, the literature offers limited evidence about the effects of the built environment on the occurrence of those crashes by injury severity. This article uses data from the Los Angeles region in 2010–2019 to explore the relationships between the built environment factors and the spatial distribution of freight truck-related crashes using XGBoost and SHAP methods. Results from the XGBoost model show that variables related to the built environment, in particular demographics, land uses and road network, are highly correlated to freight truck related crashes of all three injury types. The SHAP value plots further indicate the important nonlinear relationships between independent variables and dependent variables. This study also emphasizes the differences in modeling mechanisms between the XGBoost model and traditional statistical models. The findings will help transport planners develop operational measures for resolving the emerging freight truck related traffic safety problems in local communities.
Due to the burgeoning demand for freight movement, freight related road safety threats have been growing substantially. In spite of some research on the factors influencing freight truck-related crashes in major cities, the literature offers limited evidence about the effects of the built environment on the occurrence of those crashes by injury severity. This article uses data from the Los Angeles region in 2010-2019 to explore the relationships between the built environment factors and the spatial distribution of freight truck-related crashes using XGBoost and SHAP methods. Results from the XGBoost model show that variables related to the built environment, in particular demographics, land uses and road network, are highly correlated to freight truck related crashes of all three injury types. The SHAP value plots further indicate the important nonlinear relationships between independent variables and dependent variables. This study also emphasizes the differences in modeling mechanisms between the XGBoost model and traditional statistical models. The findings will help transport planners develop operational measures for resolving the emerging freight truck related traffic safety problems in local communities.Due to the burgeoning demand for freight movement, freight related road safety threats have been growing substantially. In spite of some research on the factors influencing freight truck-related crashes in major cities, the literature offers limited evidence about the effects of the built environment on the occurrence of those crashes by injury severity. This article uses data from the Los Angeles region in 2010-2019 to explore the relationships between the built environment factors and the spatial distribution of freight truck-related crashes using XGBoost and SHAP methods. Results from the XGBoost model show that variables related to the built environment, in particular demographics, land uses and road network, are highly correlated to freight truck related crashes of all three injury types. The SHAP value plots further indicate the important nonlinear relationships between independent variables and dependent variables. This study also emphasizes the differences in modeling mechanisms between the XGBoost model and traditional statistical models. The findings will help transport planners develop operational measures for resolving the emerging freight truck related traffic safety problems in local communities.
ArticleNumber 106153
Author Chen, Mingyang
Yang, Chao
Yuan, Quan
Author_xml – sequence: 1
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  surname: Yang
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  email: tongjiyc@tongji.edu.cn
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  surname: Chen
  fullname: Chen, Mingyang
  email: chen_my@tongji.edu.cn
– sequence: 3
  givenname: Quan
  surname: Yuan
  fullname: Yuan, Quan
  email: quanyuan@tongji.edu.cn
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ISSN 0001-4575
1879-2057
IngestDate Thu Oct 02 05:22:51 EDT 2025
Tue Nov 18 22:39:51 EST 2025
Sat Nov 29 06:57:43 EST 2025
Fri Feb 23 02:43:19 EST 2024
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IsScholarly true
Keywords Built environment
Freight truck-related crashes
XGBoost
Injury severity
Shapley Additive exPlanations
Zero Inflated Poisson regression
Language English
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crossref_primary_10_1016_j_aap_2021_106153
crossref_citationtrail_10_1016_j_aap_2021_106153
elsevier_sciencedirect_doi_10_1016_j_aap_2021_106153
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PublicationDate 2021-08-01
PublicationDateYYYYMMDD 2021-08-01
PublicationDate_xml – month: 08
  year: 2021
  text: 2021-08-01
  day: 01
PublicationDecade 2020
PublicationTitle Accident analysis and prevention
PublicationYear 2021
Publisher Elsevier Ltd
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Snippet •Spatial distribution of freight truck related crashes differs by injury severity.•In contrast with ZIP model, XGBoost model is used to test nonlinear...
Due to the burgeoning demand for freight movement, freight related road safety threats have been growing substantially. In spite of some research on the...
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StartPage 106153
SubjectTerms Built environment
Freight truck-related crashes
Injury severity
Shapley Additive exPlanations
XGBoost
Zero Inflated Poisson regression
Title The application of XGBoost and SHAP to examining the factors in freight truck-related crashes: An exploratory analysis
URI https://dx.doi.org/10.1016/j.aap.2021.106153
https://www.proquest.com/docview/2532241817
Volume 158
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