Using Big Data Mining Algorithm to Improve the Accuracy of Risk Assessment of Key Operating Vehicles

Traditional risk assessment methods face challenges when dealing with massive data and complex operating environment. Therefore, this paper proposes a hybrid mining algorithm-Integrated Risk Decision Tree (IRDT) which integrates decision tree and random forest. IRDT algorithm combines the intuitive interpretation of decision tree and the integrated learning advantages of random forest, and improves the accuracy and stability of risk assessment through the training and integration of multiple decision tree sub-models. The experimental results show that the accuracy of IRDT algorithm on the test set reaches 90.40%, which is significantly higher than that of logistic regression model (76.80%) and support vector machine (81.20%). In addition, IRDT algorithm shows a fast response speed when dealing with real-time data, which verifies its effectiveness in complex and multi-dimensional risk assessment tasks. The risk assessment model based on IRDT algorithm constructed in this paper not only improves the accuracy of risk assessment of key operating vehicles, but also provides detailed risk classification and quantitative assessment methods, which provides strong support for the safety management of transportation industry. The research results show that big data mining algorithm has important application value in risk assessment of key operating vehicles.