The number of accidents occurring on a daily basis is also expanding at an alarming rate due to the exponentially increasing number of automobiles on the road .With the rising number of traffic incidents and deaths these days, the transportation department's ability to anticipate the number of traffic accidents over a period of time is critical for making scientific decisions. In this case, it would be beneficial to investigate the causes of accidents so that we may devise methods to reduce them. Even if the bulk of accidents are characterised by unpredictability, there is a level of regularity that is observed over time when examining the accidents that occur in a particular area. This regularity can be used to create accident prediction models and provide well-informed predictions about accident occurrences in a given area. We investigated the interrelationships between road accidents, road conditions, and the involvement of environmental elements in the occurrence of an accident in this research. Utilizing the Apriori algorithm and Support Vector Machines, we developed an accident prediction model using data mining approaches. This analysis used Bangalore road accident datasets from 2014 to 2017 that were freely available on the internet. Several stakeholders, including ut not limited to government public works agencies, contractors, and other automobile manufacturers, can benefit from the findings of this study in better developing roads and automobiles based on the estimates obtained.



Software And Hardware