Applying Data Mining Approaches for Analyzing Hazardous Materials Transportation Accidents on Different Types of Roads

With the increase in the demand for and transportation of hazardous materials (Hazmat), frequent Hazmat road transport accidents, high death tolls and property damage have caused widespread societal concern. Therefore, it is necessary to carry out risk factor analysis of Hazmat transportation; predi...

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Autores principales:	Shanshan Wei, Xiaoyan Shen, Minhua Shao, Lijun Sun
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	hazardous materials association rules mining accident prevention different road types Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/8ad39602539342f8ac426b2acd7026ea
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spelling	oai:doaj.org-article:8ad39602539342f8ac426b2acd7026ea2021-11-25T19:04:13ZApplying Data Mining Approaches for Analyzing Hazardous Materials Transportation Accidents on Different Types of Roads10.3390/su1322127732071-1050https://doaj.org/article/8ad39602539342f8ac426b2acd7026ea2021-11-01T00:00:00Zhttps://www.mdpi.com/2071-1050/13/22/12773https://doaj.org/toc/2071-1050With the increase in the demand for and transportation of hazardous materials (Hazmat), frequent Hazmat road transport accidents, high death tolls and property damage have caused widespread societal concern. Therefore, it is necessary to carry out risk factor analysis of Hazmat transportation; predict the severity of accidents; and develop targeted, extensive and refined preventive measures to guarantee the safety of Hazmat road transportation. Based on the philosophy of graded risk management, this study used a priori algorithms in association rule mining (ARM) technology to analyze Hazmat transport accidents, using road types as classification criteria to find rules that had strong associations with property-damage-only (PDO) accidents and casualty (CAS) accidents under different road types. The results indicated that accidents involving PDO had a strong association with weather (WEA), traffic signals (TS), surface conditions (SC), fatigue (FAT) and vehicle safety status (VSS), and that accidents involving CAS had a strong association with VSS, equipment safety status (ESS), time of day (TOD) and WEA when urban roads were used for Hazmat transportation. Among Hazmat transport incidents on rural roads, the incidence of PDO accidents was associated with intersections (IN), SC, WEA, vehicle type (VT), and segment type (ST), while the occurrence of CAS accidents was associated with qualification (QUA), ESS, TS, VSS, SC, WEA, TOD, and month (MON). Strong associations between the occurrence of PDO accidents and related items, such as IN, SC, WEA and FAT, and the occurrence of CAS accidents and related items, such as ESS, TOD, VSS, WEA and SC, were identified for Hazmat road transport accidents on highways. The accident characteristics exemplified by strongly correlated rules were used as the input to the prediction model. Considering the scarcity of these events, four prediction models were selected to predict the severity of Hazmat accidents on each road type employing four analyses, and the most suitable prediction model was determined based on the evaluation criteria. The results showed that extreme gradient boosting (XGBoost) is preferable for predicting the severity of Hazmat accidents occurring on urban roads and highways, while nearest neighbor classification (NNC) is more suitable for predicting the severity of Hazmat accidents occurring on rural roads.Shanshan WeiXiaoyan ShenMinhua ShaoLijun SunMDPI AGarticlehazardous materialsassociation rules miningaccident preventiondifferent road typesEnvironmental effects of industries and plantsTD194-195Renewable energy sourcesTJ807-830Environmental sciencesGE1-350ENSustainability, Vol 13, Iss 12773, p 12773 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	hazardous materials association rules mining accident prevention different road types Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
spellingShingle	hazardous materials association rules mining accident prevention different road types Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350 Shanshan Wei Xiaoyan Shen Minhua Shao Lijun Sun Applying Data Mining Approaches for Analyzing Hazardous Materials Transportation Accidents on Different Types of Roads
description	With the increase in the demand for and transportation of hazardous materials (Hazmat), frequent Hazmat road transport accidents, high death tolls and property damage have caused widespread societal concern. Therefore, it is necessary to carry out risk factor analysis of Hazmat transportation; predict the severity of accidents; and develop targeted, extensive and refined preventive measures to guarantee the safety of Hazmat road transportation. Based on the philosophy of graded risk management, this study used a priori algorithms in association rule mining (ARM) technology to analyze Hazmat transport accidents, using road types as classification criteria to find rules that had strong associations with property-damage-only (PDO) accidents and casualty (CAS) accidents under different road types. The results indicated that accidents involving PDO had a strong association with weather (WEA), traffic signals (TS), surface conditions (SC), fatigue (FAT) and vehicle safety status (VSS), and that accidents involving CAS had a strong association with VSS, equipment safety status (ESS), time of day (TOD) and WEA when urban roads were used for Hazmat transportation. Among Hazmat transport incidents on rural roads, the incidence of PDO accidents was associated with intersections (IN), SC, WEA, vehicle type (VT), and segment type (ST), while the occurrence of CAS accidents was associated with qualification (QUA), ESS, TS, VSS, SC, WEA, TOD, and month (MON). Strong associations between the occurrence of PDO accidents and related items, such as IN, SC, WEA and FAT, and the occurrence of CAS accidents and related items, such as ESS, TOD, VSS, WEA and SC, were identified for Hazmat road transport accidents on highways. The accident characteristics exemplified by strongly correlated rules were used as the input to the prediction model. Considering the scarcity of these events, four prediction models were selected to predict the severity of Hazmat accidents on each road type employing four analyses, and the most suitable prediction model was determined based on the evaluation criteria. The results showed that extreme gradient boosting (XGBoost) is preferable for predicting the severity of Hazmat accidents occurring on urban roads and highways, while nearest neighbor classification (NNC) is more suitable for predicting the severity of Hazmat accidents occurring on rural roads.
format	article
author	Shanshan Wei Xiaoyan Shen Minhua Shao Lijun Sun
author_facet	Shanshan Wei Xiaoyan Shen Minhua Shao Lijun Sun
author_sort	Shanshan Wei
title	Applying Data Mining Approaches for Analyzing Hazardous Materials Transportation Accidents on Different Types of Roads
title_short	Applying Data Mining Approaches for Analyzing Hazardous Materials Transportation Accidents on Different Types of Roads
title_full	Applying Data Mining Approaches for Analyzing Hazardous Materials Transportation Accidents on Different Types of Roads
title_fullStr	Applying Data Mining Approaches for Analyzing Hazardous Materials Transportation Accidents on Different Types of Roads
title_full_unstemmed	Applying Data Mining Approaches for Analyzing Hazardous Materials Transportation Accidents on Different Types of Roads
title_sort	applying data mining approaches for analyzing hazardous materials transportation accidents on different types of roads
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/8ad39602539342f8ac426b2acd7026ea
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Applying Data Mining Approaches for Analyzing Hazardous Materials Transportation Accidents on Different Types of Roads

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