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. 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. 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.
Keywords: Apriori algorithm; association rules mining; data mining; expressways; hazardous material.
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