Criticality trend analysis based on highway accident factors using improved data mining algorithms

Abstract

Highway accident data analysis provides probability of occurrence of road accidents by associating different accident factors using data mining algorithms. Analysis can be improved by using advanced data mining algorithms that compute relationships with minimum processing time. As accident datasets are very heterogeneous in nature, it is difficult to identify the relationship between critical factors responsible for road accidents without data mining algorithms. In this study, K-modes for clustering and frequent pattern growth algorithms to extract relationships between critical accident factors have been used. The accomplished result concludes better relationships with better accuracy than earlier implemented data mining algorithms and has found meaningful hidden situations that would be beneficial for future work in decreasing the number of highway accidents.