The major problem with road accident data analysis is its heterogeneous in nature. The main concern with road accident data analysis is to identify the most influential factors affecting road accident frequency and accident severity. Therefore, an comparative study of road accident data is required to identify the several factors associated with road accidents. Therefore, it is an important task for the safety analysis to carry out a comparative study of road accidents to identify the factors that causes an accident to happen, so that preventive actions can be taken to overcome the accident rate and severity of accidents consequences.
It not only concern with health issues but also associated with economic burden on the society. The increasing number of road and traffic accidents is a challenging issue to the transportation systems. The generated results for each cluster and EDS proves that heterogeneity exists in the entire dataset and clustering prior to analysis certainly reduces heterogeneity fromthe dataset and provides better solutions. However, in this certain techniques these are well suited to remove heterogeneity of road accident data. Frequent Pattern (FP) growth technique is applied on the clusters formed and entire dataset (EDS). Initially,the LCC and k-modes clustering technique are applied on road accident data to form different clusters. In this, we are making use of latent class clustering (LCC) and k-modes clustering technique on a new road accident data. The heterogeneity of road accident data is a big challenge in road safety analysis. Road accident data analysis is a very important means to identify various factors associated with road accidents and can help in reducing the accident rate. Road safety is a term associated with the planning and implementing certain strategy to overcome the road and traffic accidents. ***Assistant Professor, Department of Computer Science and Engineering IRTT, Erode.Ībstract:- Road accidents are one of the most imperative factors that affect the untimely death among people and economic loss of public and private property. K.S.R College of Engineering, Tiruchengode. **Associate Professor, Department of Computer Science and Engineering *Final year, Department of Computer Science and Engineering Ram Prasanth T*, Spanglar Diaz V*, Surendran N*, Udhayavel V*, Dr. A Statistical Analysis of Road Accident Data-Using Data Mining Techniques
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