• Swastik K
• Hitha M V
• Varun C U
• Akash Anilkumar Rampur
• Dr. S Manjula

Traffic accidents on city streets are the consequence of coordinated activities by various variables, including humans, vehicles, roadways, and the environment. In order to find the key causes of these accidents, it is necessary to mine the relationship rules between significant risk factors from the statistics on these incidences.. This research advances the Apriori algorithm for mining risk factor connection rules and delves into the causes of traffic accidents on city streets, taking into consideration the many layers and dimensions of accident data. To develop qualifying association rules between risk variables, metrics such as support, confidence, and lift were adjusted depending to the layer and dimension of individual attributes. After then, the data was filtered to create a set of useful association rules.. The study's findings help the traffic department to establish effective accident prevention strategies and improve traffic safety on city streets.

Urban roads, causes of traffic accidents, data mining, association rules and Apriori algorithm.

"CAUSE ANALYSIS OF ACCIDENTS USING RULE MINING ALGORITHM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 6, page no.e448-e453, June-2021, Available :http://www.jetir.org/papers/JETIR2106618.pdf

"CAUSE ANALYSIS OF ACCIDENTS USING RULE MINING ALGORITHM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 6, page no. ppe448-e453, June-2021, Available at : http://www.jetir.org/papers/JETIR2106618.pdf