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Application of association rules mining algorithm for hazardous materials transportation crashes on expressway
Jungyeol Hong1, Reuben Tamakloe1, Dongjoo Park1
1Department of Transportation Engineering, The University of Seoul, 163 Seoulsiripdae-ro Dongdaemun-gu, Seoul 02504, South Korea.
Accident; Analysis and Prevention
|May 23, 2020
Summary
Hazardous material (HAZMAT) vehicle crashes on expressways, though infrequent, cause severe damage. Association rules mining identified key risk factors like male drivers and clear weather, enabling targeted safety improvements.
Area of Science:
- Road safety
- Transportation engineering
- Data mining
Background:
- Crashes involving hazardous material (HAZMAT) vehicles are infrequent but have high societal impact.
- Identifying leading causes of HAZMAT crashes is crucial for expressway safety improvement.
Purpose of the Study:
- To discover contributory crash-risk factors for HAZMAT vehicle-involved crashes on expressways using association rules mining (ARM).
Main Methods:
- Utilized the Apriori algorithm for association rules mining on a HAZMAT crash database (2008-2017).
- Analyzed 855 generated rules to identify significant crash patterns and risk factors.
Main Results:
- HAZMAT crashes are strongly associated with male drivers, single-vehicle incidents, clear weather, daytime, and mainline expressway segments.
- Specific associations were found for HAZMAT tank-lorry and cargo truck crashes, single-vehicle incidents, and mainline crashes.
Conclusions:
- ARM is an effective data mining technique for understanding HAZMAT crash-risk factors.
- The study provides actionable insights for enhancing expressway safety through targeted countermeasures.
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