Abstract: Traffic accidents on city streets are the consequence of coordinated activities by numerous elements, including humans, vehicles, roadways, and the environment. It is important to mine the connection rules between significant risk factors from the statistics on these incidents in order to determine the primary causes of these accidents. This method enhances the Apriori algorithm to mine the association rules between risk factors and probes deep into the causes of traffic accidents on urban roads, taking into account the many layers and dimensions of accident data. The parameters like support, confidence, and lift were modified according to the layer and dimension of certain characteristics in order to find qualifying association rules between risk factors. The findings were then filtered to provide a set of useful association rules. The system's data allow the traffic department to develop appropriate accident-prevention strategies and improve traffic safety on city streets. The major goal of the system is to discover the link between traffic accident risk variables and accident kinds. By analysing the key accident variables, the system was built as an automation to decrease road accidents
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DOI:
10.17148/IJARCCE.2021.106119
