Road accident analysis is very challenging task and investigating the dependencies between the attributes become complex because of many environmental and road related factors. An exhaustive research is being conducted to identify the optimal factors which influence fatal accidents. In this paper we propose a novel methodology called Voting Algorithm for Aggregated Feature Selection (VAAFS) which selects an optimal number of significant features with majority votes identified by more than one Feature Mining algorithms. The optimal features selected by VAAFS will be then extended to the classifiers over an Indian road accident data set obtained from the Coimbatore City Traffic Head Quarters, Tamilnadu, India and with international datasets obtained from Fatality Analysis Reporting System (FARS), USA, and the STATS19 data collection system, maintained by the of United Kingdom (UK) to model the accident severity. The output from VAAFS shows that type of vehicle, high risk road users like pedestrian and two wheelers, young road users, government holidays, selected week days, manner of collision, seating position etc. are most significant factors in modeling Accident Severity. The proposed method is highly successful in reducing misclassification error rate and to improve the predictive accuracy with optimal features than the previous studies. It seems very promising for observing road accident patterns.
Chi-Square, Classification, Ensemble, Fatality, Feature Selection, Majority Voting, Road Accidents.
R. GEETHA RAMANI, Shanthi SELVARAJ, "A Pragmatic Approach for Refined Feature Selection for the Prediction of Road Accident Severity", Studies in Informatics and Control, ISSN 1220-1766, vol. 23(1), pp. 41-52, 2014. https://doi.org/10.24846/v23i1y201405
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