Road Accident Perusal Using Machine Learning Algorithms
DOI:
https://doi.org/10.61841/8vrqtc33Keywords:
Road Accident Data Analysis, Data Mining, K-Means, Apriori AlgorithmAbstract
Road accidents have been a major concerning issue in India. Approximately 5 lakh citizens are reported to be the victims of road accidents in a year. The consequences of these accidents are not only monetary losses but non-monetary losses as well. The families of the victim are affected as well. Accidents might also lead to blockage of roads, which would result in difficulties for a citizen to follow his common routine, such as travelling to work. The high volume of road traffic in India due to the enormous population is also a reason why a single road accident might lead to a big ruckus. This concern is quite prevalent in India and hence, needs to be redressed immediately. To counter this problematic situation, road accident data will be analyzed and mined, and algorithms like the K-means++ method and the Apriori algorithm will be used to analyze the dataset provided by the Indian government. We intend to determine the factors causing these accidents corresponding to every region in India, as well as the severity of each factor. In this process, we are going to determine that K-Means++ provides better results as compared to the standard K-Means. The obtained results can be used to plan preventive measures, ensuring the reduction in road accidents. Using this, causes of accidents can be ranked considering various parameters in order to estimate plans to reduce road accidents.
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