New Risk Management Data Have Been Reported by Researchers at University of Seoul (Application of association rules mining algorithm for hazardous materials transportation crashes on expressway)

2020 JUN 02 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- Investigators publish new report on Risk Management. According to news reporting originating from Seoul, South Korea, by NewsRx correspondents, research stated, “Although crashes involving hazardous material (HAZMAT) vehicles on expressways do not occur frequently compared with other types of vehicles, the number of lives lost and social damage is very high when a HAZMAT vehicle-involved crash occurs. Therefore, it is essential to identify the leading causes of crashes involving HAZMAT vehicles and make specific countermeasures to improve the safety of expressways.”

Financial supporters for this research include National Research Foundation of Korea, Korean government.

Our news editors obtained a quote from the research from the University of Seoul, “This study aims to employ the association rules mining (ARM) approach to discover the contributory crash-risk factors of HAZMAT vehicle-involved crashes on expressways. A case study is conducted using crash data obtained from the Korea Expressway Corporation crash database from 2008 to 2017. ARM was conducted using the Apriori algorithm, and a total of 855 interesting rules were generated. With appropriate support, confidence, and lift values, we found hidden patterns in the HAZMAT crash characteristics. The results indicate that HAZMAT vehicle-involved crashes are highly associated with male drivers, single vehicle-involved crashes, clear weather conditions, daytime, and mainline segments. Also, we found that HAZMAT tank-lorry and cargo truck crashes, single vehicle-involved crashes, and crashes on mainline segments of expressways had independent and unique association rules.”

According to the news editors, the research concluded: “The finding from this study demonstrates that ARM is a plausible data mining technique that can be employed to draw relationships between HAZMAT vehicle-involved crashes and significant crash-risk factors, and has the potential of providing more easy-to-understand results and relevant insights for the safety improvement of expressways.”

For more information on this research see: Application of association rules mining algorithm for hazardous materials transportation crashes on expressway. Accident Analysis & Prevention, 2020;142():105497. Accident Analysis & Prevention can be contacted at: Pergamon-Elsevier Science LTD, the Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, England. (Elsevier - www.elsevier.com; Accident Analysis & Prevention - http://www.journals.elsevier.com/accident-analysis-and-prevention/)

The news editors report that additional information may be obtained by contacting J. Hong, Dept. of Transportation Engineering, University of Seoul, 163 Seoulsiripdae-ro Dongdaemun-gu, Seoul 02504, South Korea. Additional authors for this research include R. Tamakloe and D. Park.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1016/j.aap.2020.105497. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.

Publisher contact information for the journal Accident Analysis & Prevention is: Pergamon-Elsevier Science LTD, the Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, England.

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