Incorrect attribute value detection for traffic accident data

Abstract

Safe and sustainable road systems is a common goal in all countries. Applications to assist with road asset management and crash minimization are sought universally. Road accident is a special case of trauma that constitutes a major cause of disability, untimely death and loss of loved ones as well as family bread winners. Therefore, methods to reduce accident severity are of great interest to traffic agencies and the public at large. To analysis the traffic accident factors effectively we need a noise-free dataset. Road accident fatality rate depends on many factors and it is a very challenging task to investigate the dependencies between the attributes because of the many environmental and road accident factors. Noisy data in the dataset could obscure the discovery of important factors and mislead conclusions. In this paper, we present a novel approach called Neural network and Co-appearance based analysis for Noisy Attributes values Identification (NCNAI). NCNAI separates noisy records from clean records and also identifies the incorrect attributes values. We evaluate our algorithm using two publicly available traffic accident databases of United States, one is the largest open federal database (explore.data.gov) in United States and another one is based on the National Incident Based Reporting System (NIBRS) of city and county of Denver (data.opencolorado.org). We compare our technique with three existing methods using several well-known evaluation criteria, i.e. value based Error Recall (vER), value based Error Precision (vEP), record based Error Recall (rER), record based Error Precision (rEP), and record Removal Ratio (rRR). Our results indicate that the proposed method performs significantly better than the three existing algorithms.