Abstract
Death, injury and disability from road traffic crashes continue to be a major global public health problem. Recent data suggest that the number of fatalities from traffic crashes is in excess of 1.25 million people each year with non-fatal injuries affecting a further 20-50 million people. It is predicted that by 2030 road traffic accidents will have progressed to be the 5th leading cause of death and that the number of people who will die annually from traffic accidents will have doubled from current levels. Therefore, methods to reduce accident severity are of great interest to traffic agencies and the public at large. 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. Any missing data in the database could obscure the discovery of important factors and lead to invalid conclusions. In order to make the traffic accident datasets useful for analysis, it should be preprocessed properly. In this paper, we present a novel method based on sampling of distributions obtained from correlation measures for the imputation of missing values to improve the quality of the traffic accident data. We evaluated our algorithm using two publicly available traffic accident databases of United States (explore.data.gov, data. opencolorado.org). Our results indicate that the proposed method performs significantly better than the three existing algorithms.
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Deb, R., Wee-Chung Liew, A., Oh, E. (2014). A Correlation Based Imputation Method for Incomplete Traffic Accident Data. In: Pham, DN., Park, SB. (eds) PRICAI 2014: Trends in Artificial Intelligence. PRICAI 2014. Lecture Notes in Computer Science(), vol 8862. Springer, Cham. https://doi.org/10.1007/978-3-319-13560-1_77
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DOI: https://doi.org/10.1007/978-3-319-13560-1_77
Publisher Name: Springer, Cham
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