Journal of Korean Society of Transportation ISSN:1229-1366(Print) 2234-4217(Online) 대한교통학회지

Traffic accidents caused by road drops have been steadily increasing in recent years, and have become a social issue. In this study, keyword analysis using big data-based AI was conducted to prevent accidents caused by domestic road drops based on traffic accident data from 2015 to 2019. Keyword analysis performed text mining using Python. According to the analysis, the biggest causes of road drop accidents were poor loading and overloading. Consequently a crackdown on small trucks within the haulage industry is urgently needed. Meanwhile, road managers do not have the authority to crack down on the vehicles that are in violation of domestic load regulations, and the method of fixing the load has not been specifically proposed as a law or a related clause. Therefore, there is a problem in that the criteria for determining violations when cracking down on poor loading can only be changed according to the supervision of the regulator. In order to address the limits of the crackdown on poorly loaded vehicles in Korea, preparing a manual that can be used for driver training or crackdown is urgently required, also new legal regulations that dictate specific standards are needed with haste. In future research, new R&D research projects such as the prevention of road drop by classification of the type of traffic accident and analysis of road drop type data mining using big data should be proposed.