The major problem in the analysis of accident data is its heterogeneous nature.
Road and traffic accidents are defined by a set of variables which are mostly of discrete nature. Road and traffic accidents are uncertain and unpredictable incidents and their analysis requires the knowledge of the factors affecting them. Trend analysis also shows that prior segmentation of accident data is very important before analysis. Further a trend analysis have also been performed for each clusters and EDS accidents which finds different trends in different cluster whereas a positive trend is shown by EDS. The results reveal that the combination of k mode clustering and association rule mining is very inspiring as it produces important information that would remain hidden if no segmentation has been performed prior to generate association rules. The findings of cluster based analysis and entire data set analysis are then compared. Next, association rule mining are used to identify the various circumstances that are associated with the occurrence of an accident for both the entire data set (EDS) and the clusters identified by K-modes clustering algorithm. In this paper, we proposed a framework that used K-modes clustering technique as a preliminary task for segmentation of 11,574 road accidents on road network of Dehradun (India) between 20 (both included).
Data segmentation has been used widely to overcome this heterogeneity of the accident data. However, heterogeneous nature of road accident data makes the analysis task difficult. One of the key objectives in accident data analysis to identify the main factors associated with a road and traffic accident.
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