RFA and RAA: Optimal Path finding

Distractions can result in hazardous incidents that are challenging to prevent as the world's population grows. Numerous studies have been conducted to figure out how to stop the death toll. The primary focus of the vehicle's designers is the safety of the occupants, and airbags are made to increase security and save the lives of occupants but do not prevent accidents from happening. This report shows the findings from a study on using large-scale data mining techniques to analyses traffic incidents and determine the best course of action. Additionally, it shows the locations of the landmarks that are placed in the way. The Finnish Road Administration Datasets provided the study's target data for collection. The accident frequency count is a parameter used by the machine learning technique to cluster the locations. Then, to describe these Surface Conditions, we employed association rule mining. The regulations identified various risk factors for driving while intoxicated and accidents with varying frequency. Highway crossings are more dangerous for all types of fatal accidents, according to the association rules for high-frequency accident locations. The goal of this project is to create a Pathfinding Visualizer and a tool for analyzing traffic accidents that can be used to locate the best path between any landmark location so that drivers can feel secure while driving.