Road and Traffic Accident analysis by using Data Mining techniques
Road and traffic accident data analysis are one of the prime interests in the present era. It does not only related to the public health and safety concern but also associated with using latest techniques from different domains such as data mining, statistics, machine learning. Road and
traffic accident data have different nature in comparison to other real world data as road accidents are uncertain. In this study, we are comparing three different clustering techniques latent class clustering (LCC), k-modes clustering and BIRCH clustering on a road accident data from an Indian district. Further, Naïve Bayes (NB), random forest (RF) and support vector machine (SVM) classification techniques are used to classify the data based on the severity of road accidents. The experiments validate that LCC technique is more suitable to generate good clusters to achieve maximum classification accuracy.