Mining analysis of traffic accident features based on Fp-growth algorithm and Apriori algorithm

In this paper, based on data mining technology, Fp-growth algorithm and Apriori algorithm are used to mine and analyze traffic accident features. The correlation between the causes and severity of traffic accidents is revealed through association rule mining.Apriori algorithm is less computationally efficient in mining frequent itemsets through the generation and pruning steps of candidate itemsets, though with high accuracy. In contrast, the Fp-growth algorithm avoids multiple database scans by constructing an Fp-tree, which saves memory resources and improves efficiency. The model construction and evaluation part uses a multi-type variable classification model to predict the accident severity level, and evaluates the model performance through a test set, which shows excellent performance in category 1. Ultimately, this study verifies the effectiveness of Fp-growth and Apriori algorithm in traffic accident factor mining and provides a decision support tool for traffic safety management.