A novel framework to analyze road accident time series data

Abstract

Road accident data analysis plays an important role in identifying key factors associated with road accidents. These associated factors help in taking preventive measures to overcome the road accidents. Various studies have been done on road accident data analysis using traditional statistical techniques and data mining techniques. All these studies focused on identifying key factors associated with road accidents in different countries. Road accident is uncertain and unpredictable events which can occur in any circumstances. Also, road accidents do not have similar impacts in every region of the districts. There are chances that road accident rate is increasing in a certain district but it has some lower impact in other districts. Hence, the more focus on road safety should be on those regions or districts where road accident trend is increasing. Time series analysis is an important area of study which can be helpful in identifying the increasing or decreasing trends in different districts. In this paper, we have proposed a framework to analyze road accident time series data that takes 39 time series data of 39 districts of Gujrat and Uttarakhand state of India. This framework segments the time series data into different clusters. A time series merging algorithm is proposed to find the representative time series (RTS) for each cluster. This RTS is further used for trend analysis of different clusters. The results reveals that road accident trend is going to increase in certain clusters and those districts should be the prime concern to take preventive measure to overcome the road accidents.