Identifying Efficient Road Safety Prediction Model Using Data Mining Classifiers
Durga Karthik1, P. Karthikeyan2, S.Kalaivani3, K.Vijayarekha4

1Durga Karthik, Asst. Prof., Department of Computer Science and Engineering, SRC – SASTRA Deemed University, Tamil Nadu ,India.
2P. Karthikeyan, M.C.A Final Year, SRC – SASTRA Deemed University, Tamil Nadu, India.
3S.Kalaivani, B.Tech(CSE), SRC – SASTRA Deemed University , Tamil Nadu, India.
4K.Vijayarekha, Associate Dean/EEE, SASTRA Deemed University Tamil Nadu, India.

Manuscript received on 02 July 2019 | Revised Manuscript received on 09 July 2019 | Manuscript published on 30 August 2019 | PP: 1448-1454 | Volume-8 Issue-10, August 2019 | Retrieval Number: J10180881019/19©BEIESP | DOI: 10.35940/ijitee.A1018.0881019
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Abstract: Road accidents are a major cause of death and disabilities. The aim of the traffic accident analysis for a region is to investigate the cause for accidents and to determine dangerous locations in a region. Multivariate analysis of traffic accidents data is critical to identify major causes for fatal accidents. In this work, accident dataset is analysed using algorithmic approach, as an attempt to address this problem. The relationship between fatal rate and other attributes including collision manner, weather, surface condition, light condition, mobile users and drunken driving are considered. Prediction model using various data mining classifiers such as Bayesian, J48, Random Forest will be constructed to enhance safety regulations for a region.
Keywords: Collision manner, Weather, Surface condition, Bayesian, J48, Random Forest.
Scope of the Article: Data Mining Methods, Techniques, and Tools