Road Accidents Bigdata Mining and Visualization using Support Vector Machines

Authors

Usha Lokala, Wright State University - Main Campus
Srinivas Nowduri
Prabhakar K. Sharma

Document Type

Conference Proceeding

Publication Date

2017

Abstract

Useful information has been extracted from the road accident data in United Kingdom (UK), using data analytics method, for avoiding possible accidents in rural and urban areas. This analysis make use of several methodologies such as data integration, support vector machines (SVM), correlation machines and multinomial goodness. The entire datasets have been imported from the traffic department of UK with due permission. The information extracted from these huge datasets forms a basis for several predictions, which in turn avoid unnecessary memory lapses. Since data is expected to grow continuously over a period of time, this work primarily proposes a new framework model which can be trained and adapt itself to new data and make accurate predictions. This work also throws some light on use of SVM’s methodology for text classifiers from the obtained traffic data. Finally, it emphasizes the uniqueness and adaptability of SVMs methodology appropriate for this kind of research work.

Repository Citation

Lokala, U., Nowduri, S., & Sharma, P. K. (2017). Road Accidents Bigdata Mining and Visualization using Support Vector Machines. .
https://corescholar.libraries.wright.edu/knoesis/1137