Today people are suffering with road accidents in world wide. Analyzing these Road accidents are the major challenge in identifying and predicting primary features related with catastrophes. All these features are valuable for anticipatory computes to conquer road mishaps. Integrating various analytics techniques can get better model recognition and avoid road mishaps. As road safety growing quiet apprehension, speedy analytics observes all safety techniques in dynamic to spot malfunction that may signifies road mishaps on identifying key features related with road , mishaps in Telangana state. In our propose work, a framework to analyze the road mishap with classification of accidents and clustering, which analyze mishap data of Telangana stated district wise. The proposed framework describes the recommendation system for predicting road accidents. For this, classify the road accidents into fatal, major and minor. We implemented district wise data into clustering and applying enhanced k-mean algorithm. Further, implemented similarity measures to detecting the places where the severity of accidents happened and also analysing the driver behaviour analysis while accidents occur. The implementation result reveals that the road accident prediction exhibits enhance in certain areas and those areas exists in districts should be the major concern to acquire anticipatory measure to conquer the road mishaps.
Kakulapati*, V., Bharade, S., & M, Nikhil. (2020). Predicting and Preventing Recommender System for Telangana Road Accidents. International Journal of Recent Technology and Engineering (IJRTE), 8(6), 1353–1358. https://doi.org/10.35940/ijrte.f7687.038620
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