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Article: A comprehensive study of macro factors related to traffic fatality rates by XGBoost-based model and GIS techniques

TitleA comprehensive study of macro factors related to traffic fatality rates by XGBoost-based model and GIS techniques
Authors
KeywordsTraffic fatality rates
Macro factors
National scale
XGBoost
GIS
Feature importance
Issue Date2021
PublisherElsevier Ltd. The Journal's web site is located at http://www.elsevier.com/wps/find/journaldescription.cws_home/336/description#description
Citation
Accident Analysis & Prevention, 2021, v. 163, article no. 106431 How to Cite?
AbstractWith the fast development of economics, road safety is becoming a serious problem. Exploring macro factors is effective to improve road safety. However, the existing studies have some limitations: (1) The existing studies only considered one aspect of macro factors and constructed models based on a few data samples. (2) The methods commonly used cannot address the non-linear relationship or calculate the feature importance. The findings obtained from such models may be limited and biased. To address the limitations, this study proposes a BO-CV-XGBoost framework to explore the macro factors related to traffic fatality rate classes based on a high-dimensional dataset that fully considers the impact of multi-factor interaction with adequate data samples. The proposed framework is applied to a dataset in the US. 453 county-level macro factors are collected from various data sources, covering ten macro aspects, including topography, transportation, etc. The optimized BO-CV-XGBoost model obtains the best classification performance with an AUC of 0.8977 and an accuracy of 85.02%. Compared with other methods, the proposed model has superiority on fatality rate classification. Ten macro factors are identified, including ‘Current-dollar GDP’, ‘highway miles per person’, etc. The ten factors contain four aspects of information, including economics, transportation, education, and medical condition. Geographic information system (GIS) techniques are further used for spatial analysis of the identified macro factors. Therefore, targeted and effective measures are accordingly proposed to prevent traffic fatalities and improve road safety.
Persistent Identifierhttp://hdl.handle.net/10722/309881
ISSN
2023 Impact Factor: 5.7
2023 SCImago Journal Rankings: 1.897
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorJiang, F-
dc.contributor.authorMa, J-
dc.date.accessioned2022-01-10T09:15:12Z-
dc.date.available2022-01-10T09:15:12Z-
dc.date.issued2021-
dc.identifier.citationAccident Analysis & Prevention, 2021, v. 163, article no. 106431-
dc.identifier.issn0001-4575-
dc.identifier.urihttp://hdl.handle.net/10722/309881-
dc.description.abstractWith the fast development of economics, road safety is becoming a serious problem. Exploring macro factors is effective to improve road safety. However, the existing studies have some limitations: (1) The existing studies only considered one aspect of macro factors and constructed models based on a few data samples. (2) The methods commonly used cannot address the non-linear relationship or calculate the feature importance. The findings obtained from such models may be limited and biased. To address the limitations, this study proposes a BO-CV-XGBoost framework to explore the macro factors related to traffic fatality rate classes based on a high-dimensional dataset that fully considers the impact of multi-factor interaction with adequate data samples. The proposed framework is applied to a dataset in the US. 453 county-level macro factors are collected from various data sources, covering ten macro aspects, including topography, transportation, etc. The optimized BO-CV-XGBoost model obtains the best classification performance with an AUC of 0.8977 and an accuracy of 85.02%. Compared with other methods, the proposed model has superiority on fatality rate classification. Ten macro factors are identified, including ‘Current-dollar GDP’, ‘highway miles per person’, etc. The ten factors contain four aspects of information, including economics, transportation, education, and medical condition. Geographic information system (GIS) techniques are further used for spatial analysis of the identified macro factors. Therefore, targeted and effective measures are accordingly proposed to prevent traffic fatalities and improve road safety.-
dc.languageeng-
dc.publisherElsevier Ltd. The Journal's web site is located at http://www.elsevier.com/wps/find/journaldescription.cws_home/336/description#description-
dc.relation.ispartofAccident Analysis & Prevention-
dc.subjectTraffic fatality rates-
dc.subjectMacro factors-
dc.subjectNational scale-
dc.subjectXGBoost-
dc.subjectGIS-
dc.subjectFeature importance-
dc.titleA comprehensive study of macro factors related to traffic fatality rates by XGBoost-based model and GIS techniques-
dc.typeArticle-
dc.identifier.emailJiang, F: ffjiang@hku.hk-
dc.identifier.emailMa, J: junma@hku.hk-
dc.identifier.authorityMa, J=rp02719-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.aap.2021.106431-
dc.identifier.pmid34758411-
dc.identifier.scopuseid_2-s2.0-85118541254-
dc.identifier.hkuros331415-
dc.identifier.volume163-
dc.identifier.spagearticle no. 106431-
dc.identifier.epagearticle no. 106431-
dc.identifier.isiWOS:000721497900004-
dc.publisher.placeUnited Kingdom-

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